diff --git a/llvm/include/llvm/Analysis/EmitCModelRegistry.h b/llvm/include/llvm/Analysis/EmitCModelRegistry.h
new file mode 100644
--- /dev/null
+++ b/llvm/include/llvm/Analysis/EmitCModelRegistry.h
@@ -0,0 +1,58 @@
+//===- EmitCModelRegistry.h ---- Registry for EmitC models  -----*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+
+#ifndef LLVM_ANALYSIS_EMITCMODELREGISTRY_H
+#define LLVM_ANALYSIS_EMITCMODELREGISTRY_H
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+
+namespace llvm {
+
+template <class ModelT> class EmitCModelRegistry {
+public:
+  static EmitCModelRegistry &get() {
+    static EmitCModelRegistry Registry;
+    return Registry;
+  }
+
+  void addModel(std::unique_ptr<ModelT> Model) {
+    Models[Model->name()] = std::move(Model);
+  }
+
+  ModelT *getModel(const std::string &Name) {
+    auto itr = Models.find(Name);
+    if (itr == std::end(Models)) {
+      return nullptr;
+    }
+    return itr->second.get();
+  }
+
+private:
+  EmitCModelRegistry() {}
+
+  std::unordered_map<std::string, std::unique_ptr<ModelT>> Models;
+};
+
+template <class ModelT> class EmitCModelRegistrationHandle {
+public:
+  EmitCModelRegistrationHandle(std::unique_ptr<ModelT> Model) {
+    EmitCModelRegistry<ModelT>::get().addModel(std::move(Model));
+  }
+};
+} // namespace llvm
+
+#define REGISTER_EMITC_MODEL(BaseModelType, LocalModelType)                    \
+  namespace {                                                                  \
+  llvm::EmitCModelRegistrationHandle<BaseModelType> _handle_##LocalModelType(  \
+      std::make_unique<llvm::emitc::generated::LocalModelType>());             \
+  }
+
+#endif
diff --git a/llvm/include/llvm/Analysis/EmitCTensor.h b/llvm/include/llvm/Analysis/EmitCTensor.h
new file mode 100644
--- /dev/null
+++ b/llvm/include/llvm/Analysis/EmitCTensor.h
@@ -0,0 +1,217 @@
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// SPDX-License-Identifier: Apache-2.0
+
+// This code was originally sourced from github.com/iml130/mlir-emitc and has
+// been modified to fit the needs of generated C++ models in LLVM.
+
+#ifndef LLVM_ANALYSIS_EMITCTENSOR_H
+#define LLVM_ANALYSIS_EMITCTENSOR_H
+
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <cstddef>
+#include <functional>
+#include <numeric>
+#include <vector>
+
+// Note: it is critical to keep the llvm prefix of this namespace as the
+// autogenerated emitc  files include an embedded runtime which duplicates
+// _some_ of this code, and this namespace prefix prevents any ODR violations
+namespace llvm::emitc {
+
+namespace utility {
+template <size_t... Shape> static constexpr size_t size() {
+  constexpr std::array<size_t, sizeof...(Shape)> s = {Shape...};
+
+  size_t result = 1;
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    result *= s[i];
+  }
+  return result;
+}
+
+template <size_t... Shape>
+static constexpr std::array<size_t, sizeof...(Shape)> strides() {
+  std::array<size_t, sizeof...(Shape)> result = {};
+  constexpr std::array<size_t, sizeof...(Shape)> s = {Shape...};
+
+  if (sizeof...(Shape) == 0) {
+    return result;
+  }
+
+  result[sizeof...(Shape) - 1] = 1;
+
+  for (size_t i = sizeof...(Shape) - 1; i > 0; i--) {
+    result[i - 1] = result[i] * s[i];
+  }
+
+  return result;
+}
+
+template <size_t... Shape>
+constexpr size_t ravel_index(std::array<size_t, sizeof...(Shape)> indices) {
+  std::array<size_t, sizeof...(Shape)> shape = {Shape...};
+
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    assert(indices[i] < shape[i]);
+  }
+
+  std::array<size_t, sizeof...(Shape)> s = strides<Shape...>();
+
+  size_t result = 0;
+  for (size_t i = 0; i < indices.size(); ++i) {
+    result += indices[i] * s[i];
+  }
+
+  return result;
+}
+
+template <size_t... Shape, typename... Indices>
+constexpr size_t ravel_index(Indices... indices) {
+  static_assert(sizeof...(Indices) == sizeof...(Shape),
+                "Incorrect number of arguments");
+  return ravel_index<Shape...>({static_cast<size_t>(indices)...});
+}
+
+template <size_t... Shape>
+constexpr std::array<size_t, sizeof...(Shape)> unravel_index(size_t index) {
+  assert(index < size<Shape...>());
+
+  std::array<size_t, sizeof...(Shape)> s = strides<Shape...>();
+
+  std::array<size_t, sizeof...(Shape)> result = {};
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    result[i] = index / s[i];
+    index = index % s[i];
+  }
+
+  return result;
+}
+
+template <class...> struct conjunction : std::true_type {};
+template <class B1> struct conjunction<B1> : B1 {};
+template <class B1, class... Bn>
+struct conjunction<B1, Bn...>
+    : std::conditional_t<bool(B1::value), conjunction<Bn...>, B1> {};
+
+template <class... B> constexpr bool conjunction_v = conjunction<B...>::value;
+} // namespace utility
+
+template <typename T, size_t... Shape> class Tensor {
+public:
+  using value_type = T;
+  using reference = typename std::vector<T>::reference;
+  using iterator = typename std::vector<T>::iterator;
+  using const_iterator = typename std::vector<T>::const_iterator;
+
+  Tensor() : data(size()) {}
+
+  Tensor(std::initializer_list<T> data) : data(data) {
+    assert(data.size() == size());
+  }
+
+  Tensor(std::vector<T> data) : data(std::move(data)) {
+    assert(data.size() == size());
+  }
+
+  T *get() { return data.data(); }
+
+  static constexpr size_t dim(size_t index) {
+    assert(0 <= index && index < rank());
+    constexpr std::array<size_t, rank()> s = {Shape...};
+    return s[index];
+  }
+
+  static constexpr size_t rank() { return sizeof...(Shape); }
+
+  static constexpr std::array<size_t, rank()> shape() { return {Shape...}; }
+
+  static constexpr size_t size() { return utility::size<Shape...>(); }
+
+  static constexpr std::array<size_t, rank()> strides() {
+    return utility::strides<Shape...>();
+  }
+
+  std::vector<std::array<size_t, rank()>>
+  window(std::array<size_t, rank()> index, std::array<size_t, rank()> sizes) {
+    std::vector<std::vector<size_t>> iotas;
+    for (auto &size : sizes) {
+      std::vector<size_t> range(size);
+      std::iota(range.begin(), range.end(), 0);
+      iotas.push_back(range);
+    }
+
+    std::vector<std::array<size_t, rank()>> result;
+
+    int resultSize =
+        std::accumulate(sizes.begin(), sizes.end(), 1, std::multiplies<int>{});
+    for (int n = 0; n < resultSize; ++n) {
+      std::array<size_t, rank()> u = {};
+      div_t q{n, 0};
+      for (int i = iotas.size() - 1; 0 <= i; --i) {
+        q = div(q.quot, iotas[i].size());
+        u[i] = iotas[i][q.rem];
+      }
+
+      for (size_t i = 0; i < index.size(); ++i) {
+        u[i] += index[i];
+      }
+      result.push_back(u);
+    }
+
+    return result;
+  }
+
+  iterator begin() { return data.begin(); }
+
+  const_iterator begin() const { return data.begin(); }
+
+  iterator end() { return data.end(); }
+
+  const_iterator end() const { return data.end(); }
+
+  // Index into the flat data buffer.
+  reference operator[](size_t index) {
+    assert(0 <= index && index < size());
+    return data[index];
+  }
+
+  template <typename... Indices,
+            typename = std::enable_if<
+                utility::conjunction_v<std::is_same<size_t, Indices>...>>>
+  reference operator()(Indices... indices) {
+    static_assert(sizeof...(Indices) == rank(),
+                  "Incorrect number of arguments");
+    size_t index = ravel_index({static_cast<size_t>(indices)...});
+
+    assert(index < size());
+    return data[index];
+  }
+
+  constexpr size_t ravel_index(std::array<size_t, rank()> indices) {
+    return utility::ravel_index<Shape...>(indices);
+  }
+
+  constexpr std::array<size_t, rank()> unravel_index(size_t index) {
+    return utility::unravel_index<Shape...>(index);
+  }
+
+private:
+  std::vector<T> data;
+};
+
+} // namespace llvm::emitc
+
+#endif
diff --git a/llvm/include/llvm/Analysis/MLInlineEmitCModel.h b/llvm/include/llvm/Analysis/MLInlineEmitCModel.h
new file mode 100644
--- /dev/null
+++ b/llvm/include/llvm/Analysis/MLInlineEmitCModel.h
@@ -0,0 +1,42 @@
+//===- MLInlineEmitCModel.h -- Model for inlining EmitC Models --*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+
+#ifndef LLVM_ANALYSIS_MLINLINEEMITCMODEL_H
+#define LLVM_ANALYSIS_MLINLINEEMITCMODEL_H
+
+#include "llvm/Analysis/EmitCTensor.h"
+#include "llvm/Analysis/InlineModelFeatureMaps.h"
+
+#include <string>
+
+namespace llvm {
+
+// This is the base class
+class MLInlineOzEmitCModel {
+public:
+#define DEFINE_SETTER(cpp_name, py_name, _)                                    \
+  virtual void set_##py_name(emitc::Tensor<int64_t, 1>) = 0;
+
+  INLINE_FEATURE_ITERATOR(DEFINE_SETTER);
+  INLINE_COST_FEATURE_ITERATOR(DEFINE_SETTER);
+#undef FEATURE_SETTER
+
+  virtual void set_inlining_default(emitc::Tensor<int64_t, 1> x) {}
+  virtual void set_step_type(emitc::Tensor<int32_t, 1> x) {}
+  virtual void set_discount(emitc::Tensor<float, 1> x) {}
+  virtual void set_reward(emitc::Tensor<float, 1> x) {}
+
+  virtual std::string name() = 0;
+
+  virtual emitc::Tensor<int64_t, 1> run() = 0;
+};
+
+} // namespace llvm
+
+#endif
diff --git a/llvm/lib/Analysis/CMakeLists.txt b/llvm/lib/Analysis/CMakeLists.txt
--- a/llvm/lib/Analysis/CMakeLists.txt
+++ b/llvm/lib/Analysis/CMakeLists.txt
@@ -23,10 +23,6 @@
   endif()
 endif()
 
-if (DEFINED LLVM_HAVE_EMITC_INLINERSIZEMODEL)
-  add_compile_definitions(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
-  include_directories(${LLVM_EMITC_RUNTIME_INCLUDE_PATH})
-endif()
 
 add_llvm_component_library(LLVMAnalysis
   AliasAnalysis.cpp
@@ -152,6 +148,9 @@
   VFABIDemangling.cpp
   ${GeneratedMLSources}
 
+  models/emitc/mymodel.emitc.cpp
+  models/emitc/InlineOzTestModel.emitc.cpp
+
   ADDITIONAL_HEADER_DIRS
   ${LLVM_MAIN_INCLUDE_DIR}/llvm/Analysis
 
diff --git a/llvm/lib/Analysis/MLInlineAdvisor.cpp b/llvm/lib/Analysis/MLInlineAdvisor.cpp
--- a/llvm/lib/Analysis/MLInlineAdvisor.cpp
+++ b/llvm/lib/Analysis/MLInlineAdvisor.cpp
@@ -15,6 +15,7 @@
 #include "llvm/ADT/SCCIterator.h"
 #include "llvm/Analysis/AssumptionCache.h"
 #include "llvm/Analysis/CallGraph.h"
+#include "llvm/Analysis/EmitCModelRegistry.h"
 #include "llvm/Analysis/FunctionPropertiesAnalysis.h"
 #include "llvm/Analysis/InlineCost.h"
 #include "llvm/Analysis/InlineModelFeatureMaps.h"
@@ -31,8 +32,17 @@
 #include "llvm/Support/CommandLine.h"
 #include "MLInlinerEmitCRunner.h"
 
+
+// Start EmitC model registration
+#include "models/emitc/InlineOzTestModel.emitc.h"
+REGISTER_EMITC_MODEL(::llvm::MLInlineOzEmitCModel, InlineOzTestModel);
+// End EmitC model registration
+
 using namespace llvm;
 
+static cl::opt<std::string> MLInlineEmitCModelName(
+    "inliner-emitc-model-name", cl::Hidden, cl::desc(""));
+
 static cl::opt<std::string> InteractiveChannelBaseName(
     "inliner-interactive-channel-base", cl::Hidden,
     cl::desc(
@@ -40,6 +50,7 @@
         "have the name <inliner-interactive-channel-base>.in, while the "
         "outgoing name should be <inliner-interactive-channel-base>.out"));
 
+
 #if defined(LLVM_HAVE_TF_AOT_INLINERSIZEMODEL)
 // codegen-ed file
 #include "InlinerSizeModel.h" // NOLINT
@@ -50,13 +61,8 @@
 
 std::unique_ptr<InlineAdvisor>
 llvm::getEmitCModeAdvisor(Module &M, ModuleAnalysisManager &MAM) {
-#if defined(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
-  auto Runner = std::make_unique<EmitCModelRunner>(M.getContext(), FeatureMap, InlineDecisionSpec);
+  auto Runner = std::make_unique<EmitCModelRunner>(M.getContext(), FeatureMap, InlineDecisionSpec, MLInlineEmitCModelName);
   return std::make_unique<MLInlineAdvisor>(M, MAM, std::move(Runner));
-#else
-  M.getContext().emitError("LLVM was not built with LLVM_HAVE_EMITC_INLINERSIZEMODEL -- cannot use the EmitC inliner");
-  return nullptr;
-#endif
 }
 
 std::unique_ptr<InlineAdvisor>
diff --git a/llvm/lib/Analysis/MLInlinerEmitCRunner.h b/llvm/lib/Analysis/MLInlinerEmitCRunner.h
--- a/llvm/lib/Analysis/MLInlinerEmitCRunner.h
+++ b/llvm/lib/Analysis/MLInlinerEmitCRunner.h
@@ -10,36 +10,40 @@
 #ifndef LLVM_ANALYSIS_EMITCMODELRUNNER_H
 #define LLVM_ANALYSIS_EMITCMODELRUNNER_H
 
-#if defined(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
-
+#include "llvm/Analysis/EmitCModelRegistry.h"
+#include "llvm/Analysis/EmitCTensor.h"
+#include "llvm/Analysis/InlineModelFeatureMaps.h"
+#include "llvm/Analysis/MLInlineEmitCModel.h"
 #include "llvm/Analysis/MLModelRunner.h"
 #include "llvm/Analysis/TensorSpec.h"
 
-#include <emitc/types.h>
-
-#include "models/emitc/MLInlinerSizeModel.h"
-#include "llvm/Analysis/InlineModelFeatureMaps.h"
-
 namespace llvm {
 
 template <class T, int64_t... Dims>
-Tensor<T, Dims...> convertBufferToEmitCTensor(void *Buffer, TensorSpec Spec) {
+emitc::Tensor<T, Dims...> convertBufferToEmitCTensor(void *Buffer,
+                                                     TensorSpec Spec) {
   std::vector<T> Data;
   T *TypedBuffer = static_cast<T *>(Buffer);
   std::copy(TypedBuffer, TypedBuffer + Spec.getElementCount(),
             std::back_inserter(Data));
-  return Tensor<T, Dims...>(std::move(Data));
+  return emitc::Tensor<T, Dims...>(std::move(Data));
 }
 
 class EmitCModelRunner : public MLModelRunner {
 public:
   EmitCModelRunner(LLVMContext &Ctx, const std::vector<TensorSpec> &Inputs,
-                   const TensorSpec &Advice)
+                   const TensorSpec &Advice, std::string ModelName)
       : MLModelRunner(Ctx, MLModelRunner::Kind::EmitC, Inputs.size()),
         InputSpecs(Inputs), OutputSpec(Advice) {
     for (size_t I = 0; I < Inputs.size(); ++I) {
       setUpBufferForTensor(I, Inputs[I], nullptr);
     }
+
+    Model = EmitCModelRegistry<MLInlineOzEmitCModel>::get().getModel(ModelName);
+    if (!Model) {
+      Ctx.emitError("The requested model [" + ModelName +
+                    "] was not registered!");
+    }
   }
 
   static bool classof(const MLModelRunner *R) {
@@ -52,14 +56,15 @@
 #define FEATURE_SETTER(cpp_name, py_name, _)                                   \
   do {                                                                         \
     size_t idx = static_cast<size_t>(FeatureIndex::cpp_name);                  \
-    Model.set_##py_name(convertBufferToEmitCTensor<int64_t, 1>(                \
+    Model->set_##py_name(convertBufferToEmitCTensor<int64_t, 1>(               \
         getTensorUntyped(idx), InputSpecs[idx]));                              \
   } while (false);
 
     INLINE_FEATURE_ITERATOR(FEATURE_SETTER);
     INLINE_COST_FEATURE_ITERATOR(FEATURE_SETTER);
+
 #undef FEATURE_SETTER
-    Result = Model.action();
+    Result = Model->run();
     return static_cast<void *>(Result.get());
   }
 
@@ -67,11 +72,11 @@
   const std::vector<TensorSpec> InputSpecs;
   const TensorSpec OutputSpec;
 
-  MLInlineModel Model;
-  Tensor<int64_t, 1> Result;
+  emitc::Tensor<int64_t, 1> Result;
+
+  MLInlineOzEmitCModel *Model;
 };
 
 } // namespace llvm
 
-#endif // defined(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
 #endif
diff --git a/llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.h b/llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.h
new file mode 100644
--- /dev/null
+++ b/llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.h
@@ -0,0 +1,100 @@
+#include "llvm/Analysis/EmitCTensor.h"
+#include "llvm/Analysis/MLInlineEmitCModel.h"
+namespace llvm::emitc::generated {
+class InlineOzTestModel : public ::llvm::MLInlineOzEmitCModel {
+private:
+  ::llvm::emitc::Tensor<int64_t, 1> v1;
+  ::llvm::emitc::Tensor<int64_t, 1> v2;
+  ::llvm::emitc::Tensor<int64_t, 1> v3;
+  ::llvm::emitc::Tensor<int64_t, 1> v4;
+  ::llvm::emitc::Tensor<int64_t, 1> v5;
+  ::llvm::emitc::Tensor<int64_t, 1> v6;
+  ::llvm::emitc::Tensor<int64_t, 1> v7;
+  ::llvm::emitc::Tensor<int64_t, 1> v8;
+  ::llvm::emitc::Tensor<int64_t, 1> v9;
+  ::llvm::emitc::Tensor<int64_t, 1> v10;
+  ::llvm::emitc::Tensor<int64_t, 1> v11;
+  ::llvm::emitc::Tensor<int64_t, 1> v12;
+  ::llvm::emitc::Tensor<int64_t, 1> v13;
+  ::llvm::emitc::Tensor<int64_t, 1> v14;
+  ::llvm::emitc::Tensor<int64_t, 1> v15;
+  ::llvm::emitc::Tensor<int64_t, 1> v16;
+  ::llvm::emitc::Tensor<int64_t, 1> v17;
+  ::llvm::emitc::Tensor<int64_t, 1> v18;
+  ::llvm::emitc::Tensor<int64_t, 1> v19;
+  ::llvm::emitc::Tensor<int64_t, 1> v20;
+  ::llvm::emitc::Tensor<int64_t, 1> v21;
+  ::llvm::emitc::Tensor<int64_t, 1> v22;
+  ::llvm::emitc::Tensor<int64_t, 1> v23;
+  ::llvm::emitc::Tensor<int64_t, 1> v24;
+  ::llvm::emitc::Tensor<int64_t, 1> v25;
+  ::llvm::emitc::Tensor<int64_t, 1> v26;
+  ::llvm::emitc::Tensor<int64_t, 1> v27;
+  ::llvm::emitc::Tensor<int32_t, 1> v28;
+  ::llvm::emitc::Tensor<int64_t, 1> v29;
+  ::llvm::emitc::Tensor<int64_t, 1> v30;
+  ::llvm::emitc::Tensor<int64_t, 1> v31;
+  ::llvm::emitc::Tensor<int64_t, 1> v32;
+  ::llvm::emitc::Tensor<int64_t, 1> v33;
+  ::llvm::emitc::Tensor<int64_t, 1> v34;
+  ::llvm::emitc::Tensor<int64_t, 1> v35;
+  ::llvm::emitc::Tensor<int64_t, 1> v36;
+  ::llvm::emitc::Tensor<float, 1> v37;
+  ::llvm::emitc::Tensor<int64_t, 1> v38;
+  ::llvm::emitc::Tensor<float, 1> v39;
+
+public:
+  void set_callsite_cost(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_is_multiple_blocks(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_caller_conditionally_executed_blocks(
+      ::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_inlining_default(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_cold_cc_penalty(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_callee_conditionally_executed_blocks(
+      ::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_callee_users(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void
+  set_callee_basic_block_count(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_nr_ctant_params(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void
+  set_load_relative_intrinsic(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_jump_table_penalty(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_unsimplified_common_instructions(
+      ::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_indirect_call_penalty(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_load_elimination(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_call_penalty(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_cost_estimate(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_case_cluster_penalty(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_node_count(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_call_argument_setup(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_sroa_savings(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_lowered_call_arg_setup(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_threshold(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_dead_blocks(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_constant_args(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_sroa_losses(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void
+  set_simplified_instructions(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_num_loops(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_step_type(::llvm::emitc::Tensor<int32_t, 1> x) override;
+  void set_edge_count(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_nested_inlines(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void
+  set_caller_basic_block_count(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void
+  set_last_call_to_static_bonus(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void
+  set_nested_inline_cost_estimate(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_callsite_height(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void
+  set_constant_offset_ptr_args(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_switch_penalty(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_discount(::llvm::emitc::Tensor<float, 1> x) override;
+  void set_caller_users(::llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_reward(::llvm::emitc::Tensor<float, 1> x) override;
+  std::string name() override { return "InlineOzTestModel"; }
+  ::llvm::emitc::Tensor<int64_t, 1> run() override;
+};
+
+} // namespace llvm::emitc::generated
diff --git a/llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.cpp b/llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.cpp
new file mode 100644
--- /dev/null
+++ b/llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.cpp
@@ -0,0 +1,1454 @@
+#include "InlineOzTestModel.emitc.h"
+#include "llvm/Analysis/EmitCTensor.h"
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <cmath>
+#include <complex>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <functional>
+#include <limits>
+#include <numeric>
+#include <type_traits>
+#include <vector>
+namespace {
+namespace emitc {
+namespace utility {
+template <size_t... Shape> static constexpr size_t size() {
+  constexpr std::array<size_t, sizeof...(Shape)> s = {Shape...};
+  size_t result = 1;
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    result *= s[i];
+  }
+  return result;
+}
+template <size_t... Shape>
+static constexpr std::array<size_t, sizeof...(Shape)> strides() {
+  std::array<size_t, sizeof...(Shape)> result = {};
+  constexpr std::array<size_t, sizeof...(Shape)> s = {Shape...};
+  if (sizeof...(Shape) == 0) {
+    return result;
+  }
+  result[sizeof...(Shape) - 1] = 1;
+  for (size_t i = sizeof...(Shape) - 1; i > 0; i--) {
+    result[i - 1] = result[i] * s[i];
+  }
+  return result;
+}
+template <size_t... Shape>
+constexpr size_t ravel_index(std::array<size_t, sizeof...(Shape)> indices) {
+  std::array<size_t, sizeof...(Shape)> shape = {Shape...};
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    assert(indices[i] < shape[i]);
+  }
+  std::array<size_t, sizeof...(Shape)> s = strides<Shape...>();
+  size_t result = 0;
+  for (size_t i = 0; i < indices.size(); ++i) {
+    result += indices[i] * s[i];
+  }
+  return result;
+}
+template <size_t... Shape, typename... Indices>
+constexpr size_t ravel_index(Indices... indices) {
+  static_assert(sizeof...(Indices) == sizeof...(Shape),
+                "Incorrect number of arguments");
+  return ravel_index<Shape...>({static_cast<size_t>(indices)...});
+}
+template <size_t... Shape>
+constexpr std::array<size_t, sizeof...(Shape)> unravel_index(size_t index) {
+  assert(index < size<Shape...>());
+  std::array<size_t, sizeof...(Shape)> s = strides<Shape...>();
+  std::array<size_t, sizeof...(Shape)> result = {};
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    result[i] = index / s[i];
+    index = index % s[i];
+  }
+  return result;
+}
+} // namespace utility
+} // namespace emitc
+namespace detail {
+template <size_t N> constexpr size_t sum(const std::array<size_t, N> arr) {
+  size_t result = 0;
+  for (size_t i = 0; i < arr.size(); ++i) {
+    result += arr[i];
+  }
+  return result;
+}
+template <size_t N> constexpr size_t first(const std::array<size_t, N> arr) {
+  static_assert(N > 0, "Cannot get the first element of an empty array");
+  return arr[0];
+}
+template <size_t N> constexpr bool all_same(const std::array<size_t, N> arr) {
+  if (arr.size() == 0) {
+    return true;
+  }
+  size_t first = arr[0];
+  for (size_t i = 1; i < arr.size(); ++i) {
+    if (arr[i] != first) {
+      return false;
+    }
+  }
+  return true;
+}
+template <class...> struct conjunction : std::true_type {};
+template <class B1> struct conjunction<B1> : B1 {};
+template <class B1, class... Bn>
+struct conjunction<B1, Bn...>
+    : std::conditional_t<bool(B1::value), conjunction<Bn...>, B1> {};
+template <class... B> constexpr bool conjunction_v = conjunction<B...>::value;
+template <bool B, typename T> struct case_t {
+  static constexpr bool value = B;
+  using type = T;
+};
+template <typename First, typename... Rest>
+struct switch_t : std::conditional_t<First::value, First, switch_t<Rest...>> {};
+template <typename T> struct switch_t<T> {
+  using type = T;
+};
+template <bool B, typename T> struct switch_t<case_t<B, T>> {
+  static_assert(B, "None of the supplied conditions evaluate to true.");
+  using type = T;
+};
+} // namespace detail
+template <typename T, size_t... Shape> class _UnusedTensorType {
+public:
+  using value_type = T;
+  using reference = typename std::vector<T>::reference;
+  using iterator = typename std::vector<T>::iterator;
+  using const_iterator = typename std::vector<T>::const_iterator;
+  _UnusedTensorType() : data(size()) {}
+  _UnusedTensorType(std::initializer_list<T> data) : data(data) {
+    assert(data.size() == size());
+  }
+  _UnusedTensorType(std::vector<T> data) : data(std::move(data)) {}
+  static constexpr size_t dim(size_t index) {
+    assert(0 <= index && index < rank());
+    constexpr std::array<size_t, rank()> s = {Shape...};
+    return s[index];
+  }
+  static constexpr size_t rank() { return sizeof...(Shape); }
+  static constexpr std::array<size_t, rank()> shape() { return {Shape...}; }
+  static constexpr size_t size() { return ::emitc::utility::size<Shape...>(); }
+  static constexpr std::array<size_t, rank()> strides() {
+    return ::emitc::utility::strides<Shape...>();
+  }
+  T *get() { return data.data(); }
+  std::vector<std::array<size_t, rank()>>
+  window(std::array<size_t, rank()> index, std::array<size_t, rank()> sizes) {
+    std::vector<std::vector<size_t>> iotas;
+    for (auto &size : sizes) {
+      std::vector<size_t> range(size);
+      std::iota(range.begin(), range.end(), 0);
+      iotas.push_back(range);
+    }
+    std::vector<std::array<size_t, rank()>> result;
+    int resultSize =
+        std::accumulate(sizes.begin(), sizes.end(), 1, std::multiplies<int>{});
+    for (int n = 0; n < resultSize; ++n) {
+      std::array<size_t, rank()> u = {};
+      div_t q{n, 0};
+      for (int i = iotas.size() - 1; 0 <= i; --i) {
+        q = div(q.quot, iotas[i].size());
+        u[i] = iotas[i][q.rem];
+      }
+      for (size_t i = 0; i < index.size(); ++i) {
+        u[i] += index[i];
+      }
+      result.push_back(u);
+    }
+    return result;
+  }
+  iterator begin() { return data.begin(); }
+  const_iterator begin() const { return data.begin(); }
+  iterator end() { return data.end(); }
+  const_iterator end() const { return data.end(); }
+  reference operator[](size_t index) {
+    assert(0 <= index && index < size());
+    return data[index];
+  }
+  template <typename... Indices,
+            typename = std::enable_if<
+                detail::conjunction_v<std::is_same<size_t, Indices>...>>>
+  reference operator()(Indices... indices) {
+    static_assert(sizeof...(Indices) == rank(),
+                  "Incorrect number of arguments");
+    size_t index = ravel_index({static_cast<size_t>(indices)...});
+    assert(index < size());
+    return data[index];
+  }
+  constexpr size_t ravel_index(std::array<size_t, rank()> indices) {
+    return ::emitc::utility::ravel_index<Shape...>(indices);
+  }
+  constexpr std::array<size_t, rank()> unravel_index(size_t index) {
+    return ::emitc::utility::unravel_index<Shape...>(index);
+  }
+
+private:
+  std::vector<T> data;
+};
+template <typename T> using Tensor0D = ::llvm::emitc::Tensor<T>;
+template <typename T, size_t Dim0>
+using Tensor1D = ::llvm::emitc::Tensor<T, Dim0>;
+template <typename T, size_t Dim0, size_t Dim1>
+using Tensor2D = ::llvm::emitc::Tensor<T, Dim0, Dim1>;
+template <typename T, size_t Dim0, size_t Dim1, size_t Dim2>
+using Tensor3D = ::llvm::emitc::Tensor<T, Dim0, Dim1, Dim2>;
+template <typename T, size_t Dim0, size_t Dim1, size_t Dim2, size_t Dim3>
+using Tensor4D = ::llvm::emitc::Tensor<T, Dim0, Dim1, Dim2, Dim3>;
+template <typename T> using is_scalar = std::is_arithmetic<T>;
+template <typename T, typename Unused = void>
+struct is_tensor : std::false_type {};
+template <typename T, size_t... Shape>
+struct is_tensor<::llvm::emitc::Tensor<T, Shape...>> : std::true_type {};
+template <size_t Dim, typename T, typename Unused = void>
+struct is_tensor_of_dim : std::false_type {};
+template <size_t Dim, typename T, size_t... Shape>
+struct is_tensor_of_dim<Dim, ::llvm::emitc::Tensor<T, Shape...>> {
+  static constexpr bool value =
+      ::llvm::emitc::Tensor<T, Shape...>::rank() == Dim;
+};
+template <typename T>
+using IsScalar = typename std::enable_if_t<std::is_scalar<T>::value, bool>;
+template <typename T>
+using IsTensor = typename std::enable_if_t<is_tensor<T>::value, bool>;
+template <size_t Dim, typename T>
+using IsTensorOfDim =
+    typename std::enable_if_t<is_tensor_of_dim<Dim, T>::value, bool>;
+template <typename T> struct get_element_type {
+  using type = T;
+};
+template <typename T, size_t... Shape>
+struct get_element_type<::llvm::emitc::Tensor<T, Shape...>> {
+  using type = T;
+};
+template <typename T, typename ET>
+using IsTensorOfType = std::enable_if_t<
+    std::is_same<typename get_element_type<T>::type, ET>::value, bool>;
+template <typename Dest, typename Src> struct replace_element_type {
+  using type = Dest;
+};
+template <typename Dest, typename Src, size_t... Shape>
+struct replace_element_type<Dest, ::llvm::emitc::Tensor<Src, Shape...>> {
+  using type = ::llvm::emitc::Tensor<Dest, Shape...>;
+};
+template <typename Dest, typename Src> using UnaryFuncType = Dest (*)(Src);
+template <typename Dest, typename SrcLeft, typename SrcRight>
+using BinaryFuncType = Dest (*)(SrcLeft, SrcRight);
+template <typename Dest, typename Src, typename UnaryOp, IsScalar<Src> = true>
+inline Dest unary(const Src &x, UnaryOp &&op) {
+  return op(x);
+}
+template <typename Dest, typename Src, typename UnaryOp, IsTensor<Src> = true>
+inline Dest unary(const Src &x, UnaryOp &&op) {
+  Dest z;
+  std::transform(x.begin(), x.end(), z.begin(), op);
+  return z;
+}
+template <typename Dest, typename SrcLeft, typename SrcRight, typename BinaryOp,
+          IsScalar<SrcLeft> = true, IsScalar<SrcRight> = true>
+inline Dest binary(const SrcLeft &x, const SrcRight &y, BinaryOp &&op) {
+  return op(x, y);
+}
+template <typename Dest, typename SrcLeft, typename SrcRight, typename BinaryOp,
+          IsTensor<SrcLeft> = true, IsTensor<SrcRight> = true>
+inline Dest binary(const SrcLeft &x, const SrcRight &y, BinaryOp &&op) {
+  Dest z;
+  std::transform(x.begin(), x.end(), y.begin(), z.begin(), op);
+  return z;
+}
+template <typename Dest, typename SrcA, typename SrcB, typename SrcC,
+          typename TernaryOp, IsScalar<SrcA> = true, IsScalar<SrcB> = true,
+          IsScalar<SrcC> = true>
+inline Dest ternary(const SrcA &a, const SrcB &b, const SrcB &c,
+                    TernaryOp &&op) {
+  return op(a, b, c);
+}
+template <typename Dest, typename SrcA, typename SrcB, typename SrcC,
+          typename TernaryOp, IsTensor<SrcA> = true, IsTensor<SrcB> = true,
+          IsTensor<SrcC> = true>
+inline Dest ternary(const SrcA &a, const SrcB &b, const SrcB &c,
+                    TernaryOp &&op) {
+  Dest d;
+  auto first1 = a.begin(), last1 = a.end();
+  auto first2 = b.begin(), first3 = c.begin();
+  auto result = d.begin();
+  while (first1 != last1) {
+    *result = op(*first1, *first2, *first3);
+    ++result;
+    ++first1;
+    ++first2;
+    ++first3;
+  }
+  return d;
+}
+template <size_t Dim, typename T, typename... Ts> struct concat {};
+template <size_t Dim, typename T, size_t... Xs>
+struct concat<Dim, T, Tensor1D<T, Xs>...> {
+  static_assert(0 <= Dim && Dim < 1, "Dimension index out of bounds");
+  using type = Tensor1D<T, detail::sum<sizeof...(Xs)>({Xs...})>;
+};
+template <typename T, size_t Dim, size_t... Xs, size_t... Ys>
+struct concat<Dim, T, Tensor2D<T, Xs, Ys>...> {
+  static_assert(0 <= Dim && Dim < 2, "Dimension index out of bounds");
+  static_assert((Dim == 0 && detail::all_same<sizeof...(Ys)>({Ys...})) ||
+                    (Dim == 1 && detail::all_same<sizeof...(Xs)>({Xs...})),
+                "All dimensions except for the dimension index must match");
+  using type = typename std::conditional_t<
+      Dim == 0,
+      Tensor2D<T, detail::sum<sizeof...(Xs)>({Xs...}),
+               detail::first<sizeof...(Ys)>({Ys...})>,
+      Tensor2D<T, detail::first<sizeof...(Xs)>({Xs...}),
+               detail::sum<sizeof...(Ys)>({Ys...})>>;
+};
+template <typename T, size_t Dim, size_t... Xs, size_t... Ys, size_t... Zs>
+struct concat<Dim, T, Tensor3D<T, Xs, Ys, Zs>...> {
+  static_assert(0 <= Dim && Dim < 3, "Dimension index out of bounds");
+  using type = typename detail::switch_t<
+      detail::case_t<Dim == 0, Tensor3D<T, detail::sum<sizeof...(Xs)>({Xs...}),
+                                        detail::first<sizeof...(Ys)>({Ys...}),
+                                        detail::first<sizeof...(Zs)>({Zs...})>>,
+      detail::case_t<Dim == 1,
+                     Tensor3D<T, detail::first<sizeof...(Xs)>({Xs...}),
+                              detail::sum<sizeof...(Ys)>({Ys...}),
+                              detail::first<sizeof...(Zs)>({Zs...})>>,
+      detail::case_t<Dim == 2,
+                     Tensor3D<T, detail::first<sizeof...(Xs)>({Xs...}),
+                              detail::first<sizeof...(Ys)>({Ys...}),
+                              detail::sum<sizeof...(Zs)>({Zs...})>>>::type;
+};
+template <typename T, size_t Dim, size_t... D0, size_t... D1, size_t... D2,
+          size_t... D3>
+struct concat<Dim, T, Tensor4D<T, D0, D1, D2, D3>...> {
+  static_assert(0 <= Dim && Dim < 4, "Dimension index out of bounds");
+  using type = typename detail::switch_t<
+      detail::case_t<Dim == 0, Tensor4D<T, detail::sum<sizeof...(D0)>({D0...}),
+                                        detail::first<sizeof...(D1)>({D1...}),
+                                        detail::first<sizeof...(D2)>({D2...}),
+                                        detail::first<sizeof...(D3)>({D3...})>>,
+      detail::case_t<Dim == 1,
+                     Tensor4D<T, detail::first<sizeof...(D0)>({D0...}),
+                              detail::sum<sizeof...(D1)>({D1...}),
+                              detail::first<sizeof...(D2)>({D2...}),
+                              detail::first<sizeof...(D3)>({D3...})>>,
+      detail::case_t<Dim == 2,
+                     Tensor4D<T, detail::first<sizeof...(D0)>({D0...}),
+                              detail::first<sizeof...(D1)>({D1...}),
+                              detail::sum<sizeof...(D2)>({D2...}),
+                              detail::first<sizeof...(D3)>({D3...})>>,
+      detail::case_t<Dim == 3,
+                     Tensor4D<T, detail::first<sizeof...(D0)>({D0...}),
+                              detail::first<sizeof...(D1)>({D1...}),
+                              detail::first<sizeof...(D2)>({D2...}),
+                              detail::sum<sizeof...(D3)>({D3...})>>>::type;
+};
+namespace emitc {
+template <typename Src> inline Src abs(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::abs);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src ceil(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::ceil);
+  return unary<Src>(x, f);
+}
+template <typename Dest, typename Src> inline Dest convert(Src x) {
+  using ET_Dest = typename get_element_type<Dest>::type;
+  using ET_Src = typename get_element_type<Src>::type;
+  auto cast = [](ET_Src value) { return static_cast<ET_Dest>(value); };
+  return unary<Dest, Src, UnaryFuncType<ET_Dest, ET_Src>>(x, cast);
+}
+template <typename Src> inline Src exp(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::exp);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src floor(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::floor);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src log(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::log);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src negate(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = std::negate<ET_Src>{};
+  return unary<Src>(x, f);
+}
+template <typename Min, typename Src, typename Max>
+inline Src clamp(Min min, Src operand, Max max) {
+  static_assert(
+      std::is_same<Min, Src>::value ||
+          (is_tensor_of_dim<0, Min>::value &&
+           std::is_same<typename get_element_type<Src>::type,
+                        typename get_element_type<Min>::type>::value),
+      "Expected the same type for min and operand or a 0-dim tensor of the "
+      "same element type for min");
+  static_assert(
+      std::is_same<Max, Src>::value ||
+          (is_tensor_of_dim<0, Max>::value &&
+           std::is_same<typename get_element_type<Src>::type,
+                        typename get_element_type<Max>::type>::value),
+      "Expected the same type for min and operand or a 0-dim tensor of the "
+      "same element type for max");
+  const bool broadcast_min = !std::is_same<Min, Src>::value;
+  const bool broadcast_max = !std::is_same<Max, Src>::value;
+  Src result;
+  for (size_t index = 0; index < Src::size(); index++) {
+    const auto value_min = broadcast_min ? min[0] : min[index];
+    const auto value_max = broadcast_max ? max[0] : max[index];
+    auto value = operand[index];
+    value = value < value_min ? value_min : value;
+    value = value > value_max ? value_max : value;
+    result[index] = value;
+  }
+  return result;
+}
+template <typename Src> inline Src sqrt(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::sqrt);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src tanh(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::tanh);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src add(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = std::plus<ET_Src>{};
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src max(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f =
+      static_cast<const ET_Src &(*)(const ET_Src &, const ET_Src &)>(std::max);
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src min(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f =
+      static_cast<const ET_Src &(*)(const ET_Src &, const ET_Src &)>(std::min);
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src mul(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = std::multiplies<ET_Src>{};
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src pow(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src a, ET_Src b) -> ET_Src {
+    if (std::is_integral<ET_Src>::value) {
+      const bool negative = b < 0;
+      if (b < 0) {
+        b = -b;
+      }
+      ET_Src result = 1;
+      for (ET_Src i = 0; i < b; i++) {
+        result *= a;
+      }
+      if (negative) {
+        result = 1 / result;
+      }
+      return result;
+    } else {
+      return std::pow(a, b);
+    }
+  };
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src sub(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = std::minus<ET_Src>{};
+  return binary<Src>(x, y, f);
+}
+template <typename Dest, typename Src>
+inline Dest broadcast_in_dim(
+    Src operand,
+    ::llvm::emitc::Tensor<int64_t, Src::rank()> broadcast_dimensions) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  std::vector<size_t> retainedDimensions(Dest::rank());
+  std::iota(retainedDimensions.begin(), retainedDimensions.end(), 0);
+  retainedDimensions.erase(
+      std::remove_if(retainedDimensions.begin(), retainedDimensions.end(),
+                     [&broadcast_dimensions](size_t i) {
+                       return std::find(broadcast_dimensions.begin(),
+                                        broadcast_dimensions.end(),
+                                        i) == broadcast_dimensions.end();
+                     }),
+      retainedDimensions.end());
+  assert(retainedDimensions.size() == Src::rank());
+  Dest result;
+  for (size_t i = 0; i < result.size(); i++) {
+    auto dest_index = result.unravel_index(i);
+    std::array<size_t, Src::rank()> src_index;
+    for (size_t j = 0; j < src_index.size(); j++) {
+      src_index[j] = dest_index[broadcast_dimensions(j)];
+    }
+    for (size_t i = 0; i < src_index.size(); ++i) {
+      if (Src::shape()[i] == 1) {
+        src_index[i] = 0;
+      }
+    }
+    result[i] = operand[operand.ravel_index(src_index)];
+  }
+  return result;
+}
+template <typename Dest, typename Lhs, typename Rhs>
+Dest dot(Lhs lhs, Rhs rhs) {
+  static_assert(is_tensor_of_dim<2, Lhs>::value, "Expected 2 dimensional lhs");
+  static_assert(is_tensor_of_dim<2, Rhs>::value, "Expected 2 dimensional rhs");
+  static_assert(Lhs::dim(1) == Rhs::dim(0),
+                "Expected contracting dimension to match");
+  Dest output;
+  for (size_t m = 0; m < lhs.dim(0); m++) {
+    for (size_t n = 0; n < lhs.dim(1); n++) {
+      for (size_t k = 0; k < rhs.dim(1); k++) {
+        output(m, k) += lhs(m, n) * rhs(n, k);
+      }
+    }
+  }
+  return output;
+}
+template <typename Dest, typename Lhs, typename Rhs>
+Dest batch_matmul(Lhs lhs, Rhs rhs) {
+  static_assert(is_tensor_of_dim<3, Lhs>::value, "Expected 3 dimensional lhs");
+  static_assert(is_tensor_of_dim<3, Rhs>::value, "Expected 3 dimensional rhs");
+  static_assert(Lhs::dim(0) == Rhs::dim(0) && Lhs::dim(0) == Dest::dim(0),
+                "Expected batch dimension to match");
+  static_assert(Lhs::dim(2) == Rhs::dim(1),
+                "Expected contracting dimension to match");
+  static_assert(Dest::dim(1) == Lhs::dim(1), "Expected row dimension to match");
+  static_assert(Dest::dim(2) == Rhs::dim(2),
+                "Expected column dimension to match");
+  Dest output;
+  for (size_t b = 0; b < lhs.dim(0); b++) {
+    for (size_t m = 0; m < lhs.dim(1); m++) {
+      for (size_t n = 0; n < lhs.dim(2); n++) {
+        for (size_t k = 0; k < rhs.dim(2); k++) {
+          output(b, m, k) += lhs(b, m, n) * rhs(b, n, k);
+        }
+      }
+    }
+  }
+  return output;
+}
+template <int64_t Dimension, typename Dest, typename Src>
+inline Dest concatenate(Src input) {
+  Dest z = input;
+  return z;
+}
+template <int64_t Dimension, typename Dest, typename Src1, typename... Src>
+inline Dest concatenate(Src1 input1, Src... inputs) {
+  static_assert(sizeof...(inputs) > 0, "Wrong template specialization chosen");
+  using ET_Src = typename get_element_type<Src1>::type;
+  using Rest = typename concat<Dimension, ET_Src, Src...>::type;
+  Rest rest = concatenate<Dimension, Rest, Src...>(inputs...);
+  Dest z;
+  auto calculate_shift = [](const auto &shape) {
+    size_t shift = 1;
+    for (size_t i = Dimension; i < shape.size(); i++) {
+      shift *= shape[i];
+    }
+    return shift;
+  };
+  auto a_shift = calculate_shift(Src1::shape());
+  auto b_shift = calculate_shift(Rest::shape());
+  for (auto a_ptr = input1.begin(), b_ptr = rest.begin(), c_ptr = z.begin();
+       a_ptr != input1.end(); a_ptr += a_shift, b_ptr += b_shift) {
+    std::copy(a_ptr, a_ptr + a_shift, c_ptr);
+    c_ptr += a_shift;
+    std::copy(b_ptr, b_ptr + b_shift, c_ptr);
+    c_ptr += b_shift;
+  }
+  return z;
+}
+template <typename Dest, typename Src> inline Dest reshape(Src x) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  using ET_Src = typename get_element_type<Src>::type;
+  using ET_Dest = typename get_element_type<Dest>::type;
+  static_assert(std::is_same<ET_Src, ET_Dest>::value, "Element type mismatch");
+  static_assert(Src::size() == Dest::size(), "Tensor size mismatch");
+  Dest z;
+  std::copy(x.begin(), x.end(), z.begin());
+  return z;
+}
+template <typename Dest, typename Src, IsTensorOfDim<1, Src> = true>
+Dest slice(Src x, ::llvm::emitc::Tensor<int64_t, 1> start_indices,
+           ::llvm::emitc::Tensor<int64_t, 1> limit_indices,
+           ::llvm::emitc::Tensor<int64_t, 1> strides) {
+  Dest z;
+  size_t index = 0;
+  for (int64_t i = start_indices[0]; i < limit_indices[0]; i += strides[0]) {
+    z[index++] = x(i);
+  }
+  return z;
+}
+template <typename Dest, typename Src, IsTensorOfDim<2, Src> = true>
+Dest slice(Src x, ::llvm::emitc::Tensor<int64_t, 2> start_indices,
+           ::llvm::emitc::Tensor<int64_t, 2> limit_indices,
+           ::llvm::emitc::Tensor<int64_t, 2> strides) {
+  Dest z;
+  size_t index = 0;
+  for (int64_t i = start_indices[0]; i < limit_indices[0]; i += strides[0]) {
+    for (int64_t j = start_indices[1]; j < limit_indices[1]; j += strides[1]) {
+      z[index++] = x(i, j);
+    }
+  }
+  return z;
+}
+template <typename Dest, typename Src, IsTensorOfDim<3, Src> = true>
+Dest slice(Src x, ::llvm::emitc::Tensor<int64_t, 3> start_indices,
+           ::llvm::emitc::Tensor<int64_t, 3> limit_indices,
+           ::llvm::emitc::Tensor<int64_t, 3> strides) {
+  Dest z;
+  size_t index = 0;
+  for (int64_t i = start_indices[0]; i < limit_indices[0]; i += strides[0]) {
+    for (int64_t j = start_indices[1]; j < limit_indices[1]; j += strides[1]) {
+      for (int64_t k = start_indices[2]; k < limit_indices[2];
+           k += strides[2]) {
+        z[index++] = x(i, j, k);
+      }
+    }
+  }
+  return z;
+}
+template <typename Dest, typename Src, IsTensorOfDim<4, Src> = true>
+Dest slice(Src x, ::llvm::emitc::Tensor<int64_t, 4> start_indices,
+           ::llvm::emitc::Tensor<int64_t, 4> limit_indices,
+           ::llvm::emitc::Tensor<int64_t, 4> strides) {
+  Dest z;
+  size_t index = 0;
+  for (int64_t i = start_indices[0]; i < limit_indices[0]; i += strides[0]) {
+    for (int64_t j = start_indices[1]; j < limit_indices[1]; j += strides[1]) {
+      for (int64_t k = start_indices[2]; k < limit_indices[2];
+           k += strides[2]) {
+        for (int64_t c = start_indices[3]; c < limit_indices[3];
+             c += strides[3]) {
+          z[index++] = x(i, j, k, c);
+        }
+      }
+    }
+  }
+  return z;
+}
+template <typename Dest, typename Src>
+inline Dest
+pad(Src operand,
+    ::llvm::emitc::Tensor<typename get_element_type<Src>::type> padding_value,
+    ::llvm::emitc::Tensor<int64_t, Src::rank()> edge_padding_low,
+    ::llvm::emitc::Tensor<int64_t, Src::rank()> edge_padding_high,
+    ::llvm::emitc::Tensor<int64_t, Src::rank()> interior_padding) {
+  assert(std::all_of(interior_padding.begin(), interior_padding.end(),
+                     [](int64_t i) { return i >= 0; }));
+  assert(std::all_of(edge_padding_low.begin(), edge_padding_low.end(),
+                     [](int64_t i) { return i >= 0; }));
+  assert(std::all_of(edge_padding_high.begin(), edge_padding_high.end(),
+                     [](int64_t i) { return i >= 0; }));
+  Dest result;
+  auto interior = [&interior_padding](std::array<size_t, Src::rank()> index) {
+    for (size_t i = 0; i < index.size(); i++) {
+      if (index[i] % (interior_padding[i] + 1) != 0) {
+        return true;
+      }
+    }
+    return false;
+  };
+  auto out_of_bounds = [](std::array<size_t, Src::rank()> index) {
+    for (size_t i = 0; i < index.size(); i++) {
+      if (index[i] < 0 || index[i] >= Src::dim(i)) {
+        return true;
+      }
+    }
+    return false;
+  };
+  for (size_t i = 0; i < result.size(); i++) {
+    auto index = result.unravel_index(i);
+    for (size_t j = 0; j < index.size(); j++) {
+      index[j] -= edge_padding_low[j];
+    }
+    if (interior(index)) {
+      result[i] = padding_value();
+    } else {
+      for (size_t j = 0; j < index.size(); j++) {
+        size_t pad = interior_padding[j];
+        assert(index[j] % (pad + 1) == 0);
+        index[j] /= (pad + 1);
+      }
+      if (out_of_bounds(index)) {
+        result[i] = padding_value();
+      } else {
+        result[i] = operand[operand.ravel_index(index)];
+      }
+    }
+  }
+  return result;
+}
+} // namespace emitc
+namespace emitc {
+namespace tensor {
+template <typename T, size_t... Shape, typename... Indices>
+inline T extract(::llvm::emitc::Tensor<T, Shape...> x, Indices... indices) {
+  return x(indices...);
+}
+template <typename Dest, typename Src, IsScalar<Src> = true>
+inline Dest splat(Src x) {
+  Dest z;
+  std::fill(z.begin(), z.end(), x);
+  return z;
+}
+} // namespace tensor
+} // namespace emitc
+namespace emitc {
+namespace tosa {
+template <typename Src> inline Src abs(Src x) { return ::emitc::abs<Src>(x); }
+template <typename Dest, typename Src> inline Dest cast(Src x) {
+  return ::emitc::convert<Dest>(x);
+}
+template <typename Src> inline Src ceil(Src x) { return ::emitc::ceil<Src>(x); }
+template <typename Src>
+inline Src clamp(Src operand, typename Src::value_type min_value,
+                 typename Src::value_type max_value) {
+  ::llvm::emitc::Tensor<typename Src::value_type> min{min_value};
+  ::llvm::emitc::Tensor<typename Src::value_type> max{max_value};
+  return ::emitc::clamp(min, operand, max);
+}
+template <typename Src> inline Src clz(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  static_assert(std::is_same<ET_Src, int32_t>::value,
+                "Expected tensor of type int32_t");
+  auto f = [](ET_Src element) {
+    ET_Src count = 32;
+    while (element != 0 && count > 0) {
+      count--;
+      element >>= 1;
+    }
+    return count;
+  };
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src exp(Src x) { return ::emitc::exp<Src>(x); }
+template <typename Src> inline Src floor(Src x) {
+  return ::emitc::floor<Src>(x);
+}
+template <typename Src> inline Src log(Src x) { return ::emitc::log<Src>(x); }
+template <typename Src> inline Src negate(Src x) { return ::emitc::negate(x); }
+template <typename Src> inline Src reciprocal(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src element) { return (static_cast<ET_Src>(1.0) / element); };
+  return unary<Src>(x, f);
+}
+template <typename Dest, size_t Dim, typename Src>
+inline Dest rescale(Src x, typename get_element_type<Src>::type in_zp,
+                    typename get_element_type<Dest>::type out_zp,
+                    Tensor1D<int32_t, Dim> mult, Tensor1D<int32_t, Dim> shift,
+                    bool scale32, bool double_round, bool per_channel) {
+  using ET_Dest = typename get_element_type<Dest>::type;
+  using Dest_I32 = typename replace_element_type<int32_t, Dest>::type;
+  assert(!(!scale32 && double_round) &&
+         "Invalid combination of `scale32` and `double_round` arguments.");
+  auto apply_scale = [=](int64_t element, int64_t mult, int64_t shift) {
+    int64_t round = 1 << (shift - 1);
+    if (double_round && shift > 31) {
+      if (element >= 0)
+        round += 1 << 30;
+      else
+        round -= 1 << 30;
+    }
+    int64_t result = (element * mult + round) >> shift;
+    return static_cast<int32_t>(result);
+  };
+  Dest_I32 result;
+  for (size_t i = 0; i < x.size(); ++i) {
+    size_t index = per_channel ? x.unravel_index(i)[x.rank() - 1] : 0;
+    int64_t element = x[i] - in_zp;
+    int32_t scaled_element = apply_scale(element, mult[index], shift[index]);
+    result[i] = scaled_element + out_zp;
+  }
+  Tensor0D<int32_t> min{
+      static_cast<int32_t>(std::numeric_limits<ET_Dest>::min())};
+  Tensor0D<int32_t> max{
+      static_cast<int32_t>(std::numeric_limits<ET_Dest>::max())};
+  return cast<Dest>(::emitc::clamp(min, result, max));
+}
+template <typename Src> inline Src tanh(Src x) { return ::emitc::tanh<Src>(x); }
+template <typename Src> inline Src add(Src x, Src y) {
+  return ::emitc::add<Src>(x, y);
+}
+template <typename Src>
+inline Src arithmetic_right_shift(Src x, Src y, bool round) {
+  using ET_Src = typename get_element_type<Src>::type;
+  std::function<ET_Src(ET_Src, ET_Src)> f;
+  if (round) {
+    f = [](ET_Src left, ET_Src right) {
+      ET_Src result = left >> right;
+      if (right > 0 && ((left >> (right - 1)) & 1) != 0) {
+        result++;
+      }
+      return result;
+    };
+  } else {
+    f = [](ET_Src left, ET_Src right) { return left >> right; };
+  }
+  return binary<Src>(x, y, f);
+}
+template <typename Dest, typename Src> inline Dest equal(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src left, ET_Src right) { return left == right; };
+  return binary<Dest, Src>(x, y, f);
+}
+template <typename Dest, typename Src> inline Dest greater_equal(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src left, ET_Src right) { return left >= right; };
+  return binary<Dest, Src>(x, y, f);
+}
+template <typename Src> inline Src logical_left_shift(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src left, ET_Src right) { return left << right; };
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src mul(Src x, Src y) {
+  return ::emitc::mul(x, y);
+}
+template <typename Src> inline Src maximum(Src x, Src y) {
+  return ::emitc::max(x, y);
+}
+template <typename Src> inline Src minimum(Src x, Src y) {
+  return ::emitc::min(x, y);
+}
+template <typename Src, IsTensorOfType<Src, int32_t> = true>
+inline Src mul(Src x, Src y, const int32_t shift) {
+  if (shift > 0) {
+    auto f = [&shift](int32_t x, int32_t y) -> int32_t {
+      int64_t result;
+      int64_t round = 1L << (shift - 1);
+      result = x * y + round;
+      result = result >> shift;
+      return static_cast<int32_t>(result);
+    };
+    return binary<Src>(x, y, f);
+  } else {
+    return ::emitc::mul(x, y);
+  }
+}
+template <typename Src> inline Src pow(Src x, Src y) {
+  return ::emitc::pow(x, y);
+}
+template <typename Src> inline Src sub(Src x, Src y) {
+  return ::emitc::sub<Src>(x, y);
+}
+template <size_t... Shape>
+inline ::llvm::emitc::Tensor<int8_t, Shape...>
+table(::llvm::emitc::Tensor<int8_t, Shape...> x, Tensor1D<int8_t, 256> table) {
+  auto f = [&table](int8_t element) {
+    return table(static_cast<int16_t>(element) + 128);
+  };
+  return unary<::llvm::emitc::Tensor<int8_t, Shape...>>(x, f);
+}
+template <size_t... Shape>
+inline ::llvm::emitc::Tensor<int32_t, Shape...>
+table(::llvm::emitc::Tensor<int16_t, Shape...> x,
+      Tensor1D<int16_t, 513> table) {
+  auto f = [&table](int16_t element) {
+    int32_t integer = (element >> 7) + 0x100;
+    int32_t fractional = element & 0x7F;
+    int32_t result_integer = table(integer);
+    int32_t result_fractional =
+        (table(integer + 1) - table(integer)) * fractional;
+    return (result_integer << 7) + result_fractional;
+  };
+  return unary<::llvm::emitc::Tensor<int32_t, Shape...>>(x, f);
+}
+template <typename Dest, typename SrcPred, typename SrcOperand>
+inline Dest select(SrcPred a, SrcOperand b, SrcOperand c) {
+  using ET_Src_Pred = typename get_element_type<SrcPred>::type;
+  static_assert(std::is_same<ET_Src_Pred, bool>::value,
+                "Pred tensor type must be bool");
+  using ET_Src_Operand = typename get_element_type<SrcOperand>::type;
+  auto f = [](ET_Src_Pred pred, ET_Src_Operand on_true,
+              ET_Src_Operand on_false) { return pred ? on_true : on_false; };
+  return ternary<Dest, SrcPred, SrcOperand, SrcOperand>(a, b, c, f);
+}
+template <int32_t Dimension, typename Dest, typename... Src>
+inline Dest concat(Src... inputs) {
+  return ::emitc::concatenate<Dimension, Dest, Src...>(inputs...);
+}
+template <typename Dest, typename Src, typename Weights>
+Dest conv2d(Src input, Weights weights, Tensor1D<int64_t, 4> padding,
+            Tensor1D<int64_t, 2> stride, Tensor1D<int64_t, 2> dilation) {
+  static_assert(is_tensor_of_dim<4, Src>::value,
+                "Expected 4 dimensional input");
+  static_assert(is_tensor_of_dim<4, Dest>::value,
+                "Expected 4 dimensional output");
+  static_assert(is_tensor_of_dim<4, Weights>::value,
+                "Expected 4 dimensional weights");
+  assert(stride[0] > 0);
+  assert(stride[1] > 0);
+  assert(dilation[0] == 1);
+  assert(dilation[1] == 1);
+  const int N = input.dim(0);
+  const int H_IN = input.dim(1);
+  const int W_IN = input.dim(2);
+  const int C_IN = input.dim(3);
+  Dest output;
+  const int C_OUT = output.dim(3);
+  const int K_H = weights.dim(1);
+  const int K_W = weights.dim(2);
+  const int S_H = stride[0];
+  const int S_W = stride[1];
+  const int pt = padding[0];
+  const int pb = padding[1];
+  const int pl = padding[2];
+  const int pr = padding[3];
+  const int H_PAD = pt + H_IN + pb;
+  const int W_PAD = pl + W_IN + pr;
+  for (int n = 0; n < N; n++) {
+    for (int h_pad = 0; h_pad < H_PAD - K_H + 1; h_pad += S_H) {
+      for (int w_pad = 0; w_pad < W_PAD - K_W + 1; w_pad += S_W) {
+        for (int kh = 0; kh < K_H; kh++) {
+          for (int kw = 0; kw < K_W; kw++) {
+            for (int c_in = 0; c_in < C_IN; c_in++) {
+              for (int c_out = 0; c_out < C_OUT; c_out++) {
+                const int h_out = h_pad / S_H;
+                const int w_out = w_pad / S_W;
+                const int h_in = h_pad - pt + kh;
+                const int w_in = w_pad - pl + kw;
+                if (h_in < 0 || h_in >= H_IN || w_in < 0 || w_in >= W_IN)
+                  continue;
+                output(n, h_out, w_out, c_out) +=
+                    input(n, h_in, w_in, c_in) * weights(c_out, kh, kw, c_in);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return output;
+}
+template <typename Dest, typename Src, typename Weights>
+Dest depthwise_conv2d(Src input, Weights weights, Tensor1D<int64_t, 4> padding,
+                      Tensor1D<int64_t, 2> stride,
+                      Tensor1D<int64_t, 2> dilation) {
+  static_assert(is_tensor_of_dim<4, Src>::value,
+                "Expected 4 dimensional input");
+  static_assert(is_tensor_of_dim<4, Dest>::value,
+                "Expected 4 dimensional output");
+  static_assert(is_tensor_of_dim<4, Weights>::value,
+                "Expected 4 dimensional weights");
+  static_assert(Src::dim(3) == Weights::dim(2),
+                "Input channels must equal weights channels");
+  static_assert(Src::dim(0) == Dest::dim(0), "Batch sizes must be equal");
+  static_assert(Dest::dim(3) % Src::dim(3) == 0,
+                "Output channels need to be a multiple of input channels");
+  static_assert(
+      Dest::dim(3) == Src::dim(3) * Weights::dim(3),
+      "Output channels size must be input channels times channel multiplier");
+  assert(stride[0] > 0);
+  assert(stride[1] > 0);
+  assert(dilation[0] == 1);
+  assert(dilation[1] == 1);
+  const int N = input.dim(0);
+  const int H_IN = input.dim(1);
+  const int W_IN = input.dim(2);
+  const int C_IN = input.dim(3);
+  Dest output;
+  const int K_H = weights.dim(0);
+  const int K_W = weights.dim(1);
+  const int M = weights.dim(3);
+  const int S_H = stride[0];
+  const int S_W = stride[1];
+  const int pt = padding[0];
+  const int pb = padding[1];
+  const int pl = padding[2];
+  const int pr = padding[3];
+  const int H_PAD = pt + H_IN + pb;
+  const int W_PAD = pl + W_IN + pr;
+  for (int n = 0; n < N; ++n) {
+    for (int h_pad = 0; h_pad < H_PAD - K_H + 1; h_pad += S_H) {
+      for (int w_pad = 0; w_pad < W_PAD - K_W + 1; w_pad += S_W) {
+        for (int kh = 0; kh < K_H; ++kh) {
+          for (int kw = 0; kw < K_W; ++kw) {
+            for (int c_in = 0; c_in < C_IN; ++c_in) {
+              for (int m = 0; m < M; ++m) {
+                const int h_out = h_pad / S_H;
+                const int w_out = w_pad / S_W;
+                const int c_out = c_in * M + m;
+                const int h_in = h_pad - pt + kh;
+                const int w_in = w_pad - pl + kw;
+                if (h_in < 0 || h_in >= H_IN || w_in < 0 || w_in >= W_IN)
+                  continue;
+                const size_t weights_index = ::emitc::utility::ravel_index<
+                    Weights::dim(0), Weights::dim(1), 1,
+                    Weights::dim(2) * Weights::dim(3)>(kh, kw, 0, c_out);
+                output(n, h_out, w_out, c_out) +=
+                    input(n, h_in, w_in, c_in) * weights[weights_index];
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return output;
+}
+template <typename Dest, typename Src, typename Weights, typename Bias>
+Dest fully_connected(Src input, Weights weights, Bias bias) {
+  static_assert(is_tensor_of_dim<2, Src>::value,
+                "Expected 2 dimensional input");
+  static_assert(is_tensor_of_dim<2, Dest>::value,
+                "Expected 2 dimensional output");
+  static_assert(is_tensor_of_dim<2, Weights>::value,
+                "Expected 2 dimensional weights");
+  static_assert(is_tensor_of_dim<1, Bias>::value,
+                "Expected 1 dimensional bias");
+  Dest output;
+  static_assert(input.dim(0) == output.dim(0),
+                "Output and input batch dimension do not match.");
+  static_assert(input.dim(1) == weights.dim(1),
+                "Input and weights dimensions do not match.");
+  static_assert(output.dim(1) == weights.dim(0),
+                "Output and weights dimensions do not match.");
+  static_assert(weights.dim(0) == bias.dim(0),
+                "Bias and weights dimensions do not match.");
+  const size_t N = input.dim(0);
+  const size_t C_IN = input.dim(1);
+  const size_t C_OUT = weights.dim(0);
+  for (size_t n = 0; n < N; ++n) {
+    for (size_t c_out = 0; c_out < C_OUT; ++c_out) {
+      for (size_t c_in = 0; c_in < C_IN; ++c_in) {
+        auto in = input(n, c_in);
+        auto weight = weights(c_out, c_in);
+        output(n, c_out) += in * weight;
+      }
+      output(n, c_out) += bias(c_out);
+    }
+  }
+  return output;
+}
+template <typename Dest, typename Src, typename Idx,
+          IsTensorOfDim<3, Dest> = true, IsTensorOfDim<3, Src> = true,
+          IsTensorOfDim<2, Idx> = true, IsTensorOfType<Idx, int32_t> = true>
+Dest gather(Src input, Idx indices) {
+  Dest result;
+  static_assert(input.dim(0) == result.dim(0),
+                "Input and output batch dimension do not match.");
+  static_assert(input.dim(0) == indices.dim(0),
+                "Input and weight batch dimension do not match.");
+  static_assert(input.dim(2) == result.dim(2),
+                "Input and output channel dimension do not match.");
+  static_assert(indices.dim(1) == result.dim(1),
+                "Weight and output index dimension do not match.");
+  auto it = result.begin();
+  size_t d0offset = Src::dim(1) * Src::dim(2);
+  for (size_t i = 0, idx = Idx::size(); i < idx; i++) {
+    auto d0 = d0offset * (i / Idx::dim(1));
+    auto d1 = Src::dim(2) * indices[i];
+    auto start = input.begin() + d0 + d1;
+    auto end = start + Src::dim(2);
+    it = std::copy(start, end, it);
+  }
+  return result;
+}
+template <typename T, size_t B, size_t M, size_t K, size_t N>
+Tensor3D<T, B, M, N> matmul(Tensor3D<T, B, M, K> a, Tensor3D<T, B, K, N> b) {
+  return ::emitc::batch_matmul<Tensor3D<T, B, M, N>>(a, b);
+}
+namespace {
+template <typename Dest, typename Src, typename Computation>
+inline Dest reduce(Src operand, typename get_element_type<Src>::type initValue,
+                   int64_t dimension, Computation computation) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  using ET_Src = typename get_element_type<Src>::type;
+  using ET_Dest = typename get_element_type<Dest>::type;
+  static_assert(std::is_same<ET_Src, ET_Dest>::value, "Element type mismatch");
+  static_assert(Src::rank() == Dest::rank() + 1,
+                "source rank must equal dest rank + 1");
+  std::vector<size_t> retainedDimensions(Src::rank());
+  std::iota(retainedDimensions.begin(), retainedDimensions.end(), 0);
+  retainedDimensions.erase(retainedDimensions.begin() + dimension);
+  assert(retainedDimensions.size() == Dest::rank());
+  Dest result;
+  std::fill(result.begin(), result.end(), initValue);
+  for (size_t i = 0; i < operand.size(); ++i) {
+    auto value = operand[i];
+    auto index = operand.unravel_index(i);
+    std::array<size_t, Dest::rank()> reducedIndex;
+    size_t j = 0;
+    for (size_t dim : retainedDimensions) {
+      reducedIndex[j++] = index[dim];
+    }
+    auto reductionValue = result[result.ravel_index(reducedIndex)];
+    result[result.ravel_index(reducedIndex)] =
+        computation(reductionValue, value);
+  }
+  return result;
+}
+} // namespace
+template <typename Dest, typename Src>
+inline Dest argmax(Src operand, int64_t dimension) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  using ET_Src = typename get_element_type<Src>::type;
+  static_assert(Src::rank() == Dest::rank() + 1,
+                "source rank must equal dest rank + 1");
+  std::vector<size_t> retainedDimensions(Src::rank());
+  std::iota(retainedDimensions.begin(), retainedDimensions.end(), 0);
+  retainedDimensions.erase(retainedDimensions.begin() + dimension);
+  assert(retainedDimensions.size() == Dest::rank());
+  Dest result;
+  typename replace_element_type<ET_Src, Dest>::type maxValues;
+  std::fill(maxValues.begin(), maxValues.end(),
+            std::numeric_limits<ET_Src>::min());
+  for (size_t i = 0; i < operand.size(); ++i) {
+    auto value = operand[i];
+    auto index = operand.unravel_index(i);
+    std::array<size_t, Dest::rank()> reducedIndex;
+    size_t j = 0;
+    for (size_t dim : retainedDimensions) {
+      reducedIndex[j++] = index[dim];
+    }
+    auto destIndex = result.ravel_index(reducedIndex);
+    if (value > maxValues[destIndex]) {
+      maxValues[destIndex] = value;
+      result[destIndex] = index[dimension];
+    }
+  }
+  return result;
+}
+template <typename Dest, typename Src>
+inline Dest reduce_all(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  using ET_Dest = typename get_element_type<Dest>::type;
+  static_assert(std::is_same<ET_Src, bool>::value,
+                "Src tensor type must be bool");
+  static_assert(std::is_same<ET_Dest, bool>::value,
+                "Dest tensor type must be bool");
+  auto and_ = [](ET_Src a, ET_Src b) { return (a && b); };
+  return tosa::reduce<Dest, Src>(input, true, dimension, and_);
+}
+template <typename Dest, typename Src>
+inline Dest reduce_any(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  using ET_Dest = typename get_element_type<Dest>::type;
+  static_assert(std::is_same<ET_Src, bool>::value,
+                "Src tensor type must be bool");
+  static_assert(std::is_same<ET_Dest, bool>::value,
+                "Dest tensor type must be bool");
+  auto or_ = [](ET_Src a, ET_Src b) { return a || b; };
+  return tosa::reduce<Dest, Src>(input, false, dimension, or_);
+}
+template <typename Dest, typename Src>
+inline Dest reduce_max(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f =
+      static_cast<const ET_Src &(*)(const ET_Src &, const ET_Src &)>(std::max);
+  return tosa::reduce<Dest, Src>(input, std::numeric_limits<ET_Src>::min(),
+                                 dimension, f);
+}
+template <typename Dest, typename Src>
+inline Dest reduce_min(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f =
+      static_cast<const ET_Src &(*)(const ET_Src &, const ET_Src &)>(std::min);
+  return tosa::reduce<Dest, Src>(input, std::numeric_limits<ET_Src>::max(),
+                                 dimension, f);
+}
+template <typename Dest, typename Src>
+inline Dest reduce_prod(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  return tosa::reduce<Dest, Src>(input, 1, dimension,
+                                 std::multiplies<ET_Src>{});
+}
+template <typename Dest, typename Src>
+inline Dest reduce_sum(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  return tosa::reduce<Dest, Src>(input, 0, dimension, std::plus<ET_Src>{});
+}
+template <typename Dest, typename Src> inline Dest reshape(Src x) {
+  return ::emitc::reshape<Dest>(x);
+}
+template <typename Dest, typename Src>
+Dest slice(Src x, ::llvm::emitc::Tensor<int64_t, Src::rank()> start_indices,
+           ::llvm::emitc::Tensor<int64_t, Src::rank()> slice_sizes) {
+  ::llvm::emitc::Tensor<int64_t, Src::rank()> limit_indices =
+      ::emitc::add(start_indices, slice_sizes);
+  ::llvm::emitc::Tensor<int64_t, Src::rank()> strides =
+      ::emitc::tensor::splat<::llvm::emitc::Tensor<int64_t, Src::rank()>>(1);
+  return ::emitc::slice<Dest, Src>(x, start_indices, limit_indices, strides);
+}
+template <typename Dest, typename Src, typename Padding>
+inline Dest pad(Src operand, Padding padding,
+                Tensor0D<typename get_element_type<Src>::type> pad_const =
+                    Tensor0D<typename get_element_type<Src>::type>{0}) {
+  using ET_Padding = typename get_element_type<Padding>::type;
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Padding>::value, "Expected tensor argument");
+  static_assert(Padding::rank() == 2, "Padding must have rank 2");
+  static_assert(Padding::dim(0) == Src::rank(),
+                "Dimension 1 of padding must equal source rank");
+  static_assert(Padding::dim(1) == 2, "Dimension 2 of padding is must be 2");
+  static_assert(std::is_same<ET_Padding, int32_t>::value ||
+                    std::is_same<ET_Padding, int64_t>::value,
+                "Padding element type must be i32 or i64");
+  ::llvm::emitc::Tensor<int64_t, Src::rank()> edge_padding_low;
+  ::llvm::emitc::Tensor<int64_t, Src::rank()> edge_padding_high;
+  for (unsigned int i = 0; i < padding.dim(0); ++i) {
+    edge_padding_low(i) = padding(i, 0);
+    edge_padding_high(i) = padding(i, 1);
+  }
+  ::llvm::emitc::Tensor<int64_t, Src::rank()> interior_padding;
+  std::fill(interior_padding.begin(), interior_padding.end(), 0);
+  return ::emitc::pad<Dest>(operand, pad_const, edge_padding_low,
+                            edge_padding_high, interior_padding);
+}
+template <typename Dest, typename Src, IsTensorOfDim<1, Dest> = true>
+Dest tile(Src input, Tensor1D<int64_t, 1> multiples) {
+  Dest result;
+  auto it = result.begin();
+  for (int32_t i = 0, M0 = multiples[0]; i < M0; i++) {
+    it = std::copy(input.begin(), input.end(), it);
+  }
+  return result;
+}
+template <typename Dest, typename Src, IsTensorOfDim<2, Src> = true>
+Dest tile(Src input, Tensor1D<int64_t, 2> multiples) {
+  Dest result;
+  auto it = result.begin();
+  for (int32_t i = 0, M0 = multiples[0]; i < M0; i++) {
+    for (int32_t j = 0, D0 = Src::dim(0); j < D0; j++) {
+      for (int32_t k = 0, M1 = multiples[1]; k < M1; k++) {
+        auto start = input.begin() + j * Src::dim(1);
+        auto end = start + Src::dim(1);
+        it = std::copy(start, end, it);
+      }
+    }
+  }
+  return result;
+}
+template <typename Dest, typename Src, IsTensorOfDim<3, Src> = true>
+Dest tile(Src input, Tensor1D<int64_t, 3> multiples) {
+  Dest result;
+  auto it = result.begin();
+  for (int32_t m0 = 0, M0 = multiples[0]; m0 < M0; m0++) {
+    for (int32_t d0 = 0, D0 = Src::dim(0); d0 < D0; d0++) {
+      for (int32_t m1 = 0, M1 = multiples[1]; m1 < M1; m1++) {
+        for (int32_t d1 = 0, D1 = Src::dim(1); d1 < D1; d1++) {
+          for (int32_t m2 = 0, M2 = multiples[2]; m2 < M2; m2++) {
+            auto start = input.begin() + (d0 * Src::dim(1) + d1) * Src::dim(2);
+            auto end = start + Src::dim(2);
+            it = std::copy(start, end, it);
+          }
+        }
+      }
+    }
+  }
+  return result;
+}
+template <typename Dest, typename Src, IsTensorOfDim<4, Src> = true>
+Dest tile(Src input, Tensor1D<int64_t, 4> multiples) {
+  Dest result;
+  auto it = result.begin();
+  for (int32_t m0 = 0, M0 = multiples[0]; m0 < M0; m0++) {
+    for (int32_t d0 = 0, D0 = Src::dim(0); d0 < D0; d0++) {
+      for (int32_t m1 = 0, M1 = multiples[1]; m1 < M1; m1++) {
+        for (int32_t d1 = 0, D1 = Src::dim(1); d1 < D1; d1++) {
+          for (int32_t m2 = 0, M2 = multiples[2]; m2 < M2; m2++) {
+            for (int32_t d2 = 0, D2 = Src::dim(2); d2 < D2; d2++) {
+              for (int32_t m3 = 0, M3 = multiples[3]; m3 < M3; m3++) {
+                auto start =
+                    input.begin() +
+                    ((d0 * Src::dim(1) + d1) * Src::dim(2) + d2) * Src::dim(3);
+                auto end = start + Src::dim(3);
+                it = std::copy(start, end, it);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return result;
+}
+template <typename Dest, typename Src>
+inline Dest transpose(Src operand, Tensor1D<int64_t, Src::rank()> perms) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  Tensor1D<int64_t, Src::rank()> broadcast_dimensions;
+  for (size_t i = 0; i < perms.size(); ++i) {
+    auto pos = std::find(perms.begin(), perms.end(), i);
+    assert(pos != std::end(perms));
+    int64_t index = std::distance(perms.begin(), pos);
+    broadcast_dimensions[i] = index;
+  }
+  return ::emitc::broadcast_in_dim<Dest>(operand, broadcast_dimensions);
+}
+template <typename Dest, typename Src>
+inline Dest transpose(Src input, Tensor1D<int32_t, Src::rank()> perms) {
+  Tensor1D<int64_t, Src::rank()> permsInt64;
+  for (size_t i = 0; i < perms.size(); ++i) {
+    permsInt64[i] = static_cast<int64_t>(perms[i]);
+  }
+  return tosa::transpose<Dest>(input, permsInt64);
+}
+} // namespace tosa
+} // namespace emitc
+} // namespace
+namespace llvm::emitc::generated {
+void InlineOzTestModel::set_callsite_cost(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v1 = std::move(x);
+}
+void InlineOzTestModel::set_is_multiple_blocks(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v2 = std::move(x);
+}
+void InlineOzTestModel::set_caller_conditionally_executed_blocks(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v3 = std::move(x);
+}
+void InlineOzTestModel::set_inlining_default(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v4 = std::move(x);
+}
+void InlineOzTestModel::set_cold_cc_penalty(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v5 = std::move(x);
+}
+void InlineOzTestModel::set_callee_conditionally_executed_blocks(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v6 = std::move(x);
+}
+void InlineOzTestModel::set_callee_users(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v7 = std::move(x);
+}
+void InlineOzTestModel::set_callee_basic_block_count(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v8 = std::move(x);
+}
+void InlineOzTestModel::set_nr_ctant_params(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v9 = std::move(x);
+}
+void InlineOzTestModel::set_load_relative_intrinsic(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v10 = std::move(x);
+}
+void InlineOzTestModel::set_jump_table_penalty(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v11 = std::move(x);
+}
+void InlineOzTestModel::set_unsimplified_common_instructions(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v12 = std::move(x);
+}
+void InlineOzTestModel::set_indirect_call_penalty(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v13 = std::move(x);
+}
+void InlineOzTestModel::set_load_elimination(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v14 = std::move(x);
+}
+void InlineOzTestModel::set_call_penalty(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v15 = std::move(x);
+}
+void InlineOzTestModel::set_cost_estimate(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v16 = std::move(x);
+}
+void InlineOzTestModel::set_case_cluster_penalty(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v17 = std::move(x);
+}
+void InlineOzTestModel::set_node_count(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v18 = std::move(x);
+}
+void InlineOzTestModel::set_call_argument_setup(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v19 = std::move(x);
+}
+void InlineOzTestModel::set_sroa_savings(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v20 = std::move(x);
+}
+void InlineOzTestModel::set_lowered_call_arg_setup(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v21 = std::move(x);
+}
+void InlineOzTestModel::set_threshold(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v22 = std::move(x);
+}
+void InlineOzTestModel::set_dead_blocks(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v23 = std::move(x);
+}
+void InlineOzTestModel::set_constant_args(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v24 = std::move(x);
+}
+void InlineOzTestModel::set_sroa_losses(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v25 = std::move(x);
+}
+void InlineOzTestModel::set_simplified_instructions(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v26 = std::move(x);
+}
+void InlineOzTestModel::set_num_loops(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v27 = std::move(x);
+}
+void InlineOzTestModel::set_step_type(::llvm::emitc::Tensor<int32_t, 1> x) {
+  v28 = std::move(x);
+}
+void InlineOzTestModel::set_edge_count(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v29 = std::move(x);
+}
+void InlineOzTestModel::set_nested_inlines(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v30 = std::move(x);
+}
+void InlineOzTestModel::set_caller_basic_block_count(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v31 = std::move(x);
+}
+void InlineOzTestModel::set_last_call_to_static_bonus(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v32 = std::move(x);
+}
+void InlineOzTestModel::set_nested_inline_cost_estimate(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v33 = std::move(x);
+}
+void InlineOzTestModel::set_callsite_height(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v34 = std::move(x);
+}
+void InlineOzTestModel::set_constant_offset_ptr_args(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v35 = std::move(x);
+}
+void InlineOzTestModel::set_switch_penalty(
+    ::llvm::emitc::Tensor<int64_t, 1> x) {
+  v36 = std::move(x);
+}
+void InlineOzTestModel::set_discount(::llvm::emitc::Tensor<float, 1> x) {
+  v37 = std::move(x);
+}
+void InlineOzTestModel::set_caller_users(::llvm::emitc::Tensor<int64_t, 1> x) {
+  v38 = std::move(x);
+}
+void InlineOzTestModel::set_reward(::llvm::emitc::Tensor<float, 1> x) {
+  v39 = std::move(x);
+}
+::llvm::emitc::Tensor<int64_t, 1> InlineOzTestModel::run() {
+  ::llvm::emitc::Tensor<int64_t, 1> v40 = {1};
+  return v40;
+}
+
+} // namespace llvm::emitc::generated
diff --git a/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.h b/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.h
--- a/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.h
+++ b/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.h
@@ -1,19761 +1,3 @@
-#include <cstdint>
+#pragma once
 
-#include <emitc/tosa.h>
-#include <emitc/types.h>
-
-class MLInlineModel {
-private:
-  Tensor<int64_t, 1> v1;
-  Tensor<int64_t, 1> v2;
-  Tensor<int64_t, 1> v3;
-  Tensor<int64_t, 1> v4;
-  Tensor<int64_t, 1> v5;
-  Tensor<int64_t, 1> v6;
-  Tensor<int64_t, 1> v7;
-  Tensor<int64_t, 1> v8;
-  Tensor<int64_t, 1> v9;
-  Tensor<int64_t, 1> v10;
-  Tensor<int64_t, 1> v11;
-  Tensor<int64_t, 1> v12;
-  Tensor<int64_t, 1> v13;
-  Tensor<int64_t, 1> v14;
-  Tensor<int64_t, 1> v15;
-  Tensor<int64_t, 1> v16;
-  Tensor<int64_t, 1> v17;
-  Tensor<int64_t, 1> v18;
-  Tensor<int64_t, 1> v19;
-  Tensor<int64_t, 1> v20;
-  Tensor<int64_t, 1> v21;
-  Tensor<int64_t, 1> v22;
-  Tensor<int64_t, 1> v23;
-  Tensor<int64_t, 1> v24;
-  Tensor<int64_t, 1> v25;
-  Tensor<int64_t, 1> v26;
-  Tensor<int64_t, 1> v27;
-  Tensor<int32_t, 1> v28;
-  Tensor<int64_t, 1> v29;
-  Tensor<int64_t, 1> v30;
-  Tensor<int64_t, 1> v31;
-  Tensor<int64_t, 1> v32;
-  Tensor<int64_t, 1> v33;
-  Tensor<int64_t, 1> v34;
-  Tensor<int64_t, 1> v35;
-  Tensor<int64_t, 1> v36;
-  Tensor<float, 1> v37;
-  Tensor<int64_t, 1> v38;
-  Tensor<float, 1> v39;
-
-public:
-  void set_callsite_cost(Tensor<int64_t, 1> x) { v1 = std::move(x); }
-  void set_is_multiple_blocks(Tensor<int64_t, 1> x) { v2 = std::move(x); }
-  void set_caller_conditionally_executed_blocks(Tensor<int64_t, 1> x) {
-    v3 = std::move(x);
-  }
-  void set_inlining_default(Tensor<int64_t, 1> x) { v4 = std::move(x); }
-  void set_cold_cc_penalty(Tensor<int64_t, 1> x) { v5 = std::move(x); }
-  void set_callee_conditionally_executed_blocks(Tensor<int64_t, 1> x) {
-    v6 = std::move(x);
-  }
-  void set_callee_users(Tensor<int64_t, 1> x) { v7 = std::move(x); }
-  void set_callee_basic_block_count(Tensor<int64_t, 1> x) { v8 = std::move(x); }
-  void set_nr_ctant_params(Tensor<int64_t, 1> x) { v9 = std::move(x); }
-  void set_load_relative_intrinsic(Tensor<int64_t, 1> x) { v10 = std::move(x); }
-  void set_jump_table_penalty(Tensor<int64_t, 1> x) { v11 = std::move(x); }
-  void set_unsimplified_common_instructions(Tensor<int64_t, 1> x) {
-    v12 = std::move(x);
-  }
-  void set_indirect_call_penalty(Tensor<int64_t, 1> x) { v13 = std::move(x); }
-  void set_load_elimination(Tensor<int64_t, 1> x) { v14 = std::move(x); }
-  void set_call_penalty(Tensor<int64_t, 1> x) { v15 = std::move(x); }
-  void set_cost_estimate(Tensor<int64_t, 1> x) { v16 = std::move(x); }
-  void set_case_cluster_penalty(Tensor<int64_t, 1> x) { v17 = std::move(x); }
-  void set_node_count(Tensor<int64_t, 1> x) { v18 = std::move(x); }
-  void set_call_argument_setup(Tensor<int64_t, 1> x) { v19 = std::move(x); }
-  void set_sroa_savings(Tensor<int64_t, 1> x) { v20 = std::move(x); }
-  void set_lowered_call_arg_setup(Tensor<int64_t, 1> x) { v21 = std::move(x); }
-  void set_threshold(Tensor<int64_t, 1> x) { v22 = std::move(x); }
-  void set_dead_blocks(Tensor<int64_t, 1> x) { v23 = std::move(x); }
-  void set_constant_args(Tensor<int64_t, 1> x) { v24 = std::move(x); }
-  void set_sroa_losses(Tensor<int64_t, 1> x) { v25 = std::move(x); }
-  void set_simplified_instructions(Tensor<int64_t, 1> x) { v26 = std::move(x); }
-  void set_num_loops(Tensor<int64_t, 1> x) { v27 = std::move(x); }
-  void set_step_type(Tensor<int32_t, 1> x) { v28 = std::move(x); }
-  void set_edge_count(Tensor<int64_t, 1> x) { v29 = std::move(x); }
-  void set_nested_inlines(Tensor<int64_t, 1> x) { v30 = std::move(x); }
-  void set_caller_basic_block_count(Tensor<int64_t, 1> x) {
-    v31 = std::move(x);
-  }
-  void set_last_call_to_static_bonus(Tensor<int64_t, 1> x) {
-    v32 = std::move(x);
-  }
-  void set_nested_inline_cost_estimate(Tensor<int64_t, 1> x) {
-    v33 = std::move(x);
-  }
-  void set_callsite_height(Tensor<int64_t, 1> x) { v34 = std::move(x); }
-  void set_constant_offset_ptr_args(Tensor<int64_t, 1> x) {
-    v35 = std::move(x);
-  }
-  void set_switch_penalty(Tensor<int64_t, 1> x) { v36 = std::move(x); }
-  void set_discount(Tensor<float, 1> x) { v37 = std::move(x); }
-  void set_caller_users(Tensor<int64_t, 1> x) { v38 = std::move(x); }
-  void set_reward(Tensor<float, 1> x) { v39 = std::move(x); }
-  Tensor<int64_t, 1> action() {
-    Tensor<float, 1, 1> v40 = {(float)1.001000990e-03};
-    Tensor<int64_t, 1, 1, 999> v41 = {
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-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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-    Tensor<float, 1, 1> v42 = {(float)1.000100020e-04};
-    Tensor<int64_t, 1> v43 = {1};
-    Tensor<int64_t, 1> v44 = {0};
-    Tensor<float, 999> v45 = {
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-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
-        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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-        (float)-8.425087330e-01, (float)6.453705430e-01,
-        (float)-3.015207350e-01, (float)-5.132936840e-01,
-        (float)2.942183320e-01,  (float)-6.713371870e-01,
-        (float)3.467343150e-01,  (float)-8.142403360e-01,
-        (float)-7.736653680e-01, (float)-2.264990810e-01,
-        (float)-6.483533380e-01, (float)-1.229846920e-01,
-        (float)-8.217471830e-01, (float)2.703226510e-01,
-        (float)3.513719140e-02,  (float)-6.510109300e-01,
-        (float)-6.906598810e-01, (float)-3.688707050e-01,
-        (float)-9.579101800e-01, (float)-8.507568840e-01,
-        (float)3.010917310e-01,  (float)1.975249650e-01,
-        (float)7.201362250e-01,  (float)-3.384157720e-01,
-        (float)2.696233690e-01,  (float)-2.802918260e-01,
-        (float)-2.319679410e-01, (float)3.688238260e-01,
-        (float)-2.333278510e-01, (float)-2.279450000e-01,
-        (float)6.323163210e-02,  (float)-1.206192450e-01,
-        (float)6.161751740e-01,  (float)-4.157950280e-01,
-        (float)1.136372310e-01,  (float)1.284490530e-01,
-        (float)-1.384648680e-01, (float)-1.306738380e+00,
-        (float)1.065198780e-01,  (float)2.660389840e-01,
-        (float)1.070073770e-01,  (float)-4.503095150e-02,
-        (float)2.657091620e-01,  (float)4.724887310e-01,
-        (float)-8.700073510e-02, (float)1.551671330e-01,
-        (float)-4.270904360e-01, (float)1.554446670e-01,
-        (float)-1.092808620e-01, (float)2.424404320e-01,
-        (float)3.407474760e-01,  (float)-1.693004070e-01,
-        (float)3.992065190e-01,  (float)-5.221441980e-01,
-        (float)1.327640940e-02,  (float)3.855528530e-01,
-        (float)-1.129992530e-01, (float)5.407613140e-02,
-        (float)2.742832600e-01,  (float)2.237539890e-01,
-        (float)3.193411830e-01,  (float)-4.487538930e-01,
-        (float)-1.554405880e-02, (float)-2.143167260e-01,
-        (float)-1.213760300e-01, (float)1.527040900e-01,
-        (float)1.256113500e-01,  (float)4.726113750e-02};
-    Tensor<float, 20> v80 = {(float)3.355367780e-01,  (float)-1.388000880e-02,
-                             (float)6.751911040e-01,  (float)2.667974220e-03,
-                             (float)1.055345460e-01,  (float)3.203891220e-03,
-                             (float)6.174200770e-01,  (float)-1.178330560e-02,
-                             (float)-1.767182650e-01, (float)5.671358110e-02,
-                             (float)4.362215700e-01,  (float)-1.028030290e-01,
-                             (float)9.947001180e-02,  (float)5.265628100e-01,
-                             (float)6.506712440e-01,  (float)4.370287660e-01,
-                             (float)7.621463530e-01,  (float)5.977262850e-01,
-                             (float)4.100183840e-01,  (float)-1.327016650e-01};
-    Tensor<float, 2, 20> v81 = {
-        (float)7.043257950e-01,  (float)-9.608286610e-02,
-        (float)5.232187510e-01,  (float)-1.858599630e-01,
-        (float)-1.736247840e-01, (float)-3.508510590e-01,
-        (float)4.727205340e-01,  (float)-1.409519020e-01,
-        (float)-2.538996040e-01, (float)-1.509172770e-01,
-        (float)4.717685280e-01,  (float)-3.412884770e-01,
-        (float)-1.333482860e-01, (float)5.071479080e-01,
-        (float)5.508543250e-01,  (float)5.913437600e-01,
-        (float)4.606473150e-01,  (float)6.235318180e-01,
-        (float)8.454865210e-01,  (float)-2.929555180e-01,
-        (float)-7.761295430e-01, (float)1.069886090e-01,
-        (float)-5.386613610e-01, (float)1.911667440e-01,
-        (float)7.969756420e-02,  (float)9.220249200e-02,
-        (float)-5.815454130e-01, (float)4.016868770e-02,
-        (float)2.783014180e-01,  (float)1.644356550e-01,
-        (float)-5.277292730e-01, (float)1.197944950e-01,
-        (float)9.721879660e-02,  (float)-4.874985520e-01,
-        (float)-5.873894100e-01, (float)-5.865266320e-01,
-        (float)-3.519758280e-01, (float)-4.026320280e-01,
-        (float)-8.600480560e-01, (float)1.937968730e-01};
-    Tensor<float, 2> v82 = {(float)-4.473917190e-01, (float)-6.821969150e-02};
-    Tensor<int64_t, 999> v83 = emitc::tosa::cast<Tensor<int64_t, 999>>(v73);
-    Tensor<int64_t, 1, 1, 1> v84 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v38);
-    Tensor<int64_t, 1, 1, 999> v85 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v83);
-    Tensor<int64_t, 1, 1, 999> v86 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v84, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v87 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v86, v85);
-    Tensor<int32_t, 1, 1, 999> v88 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v87);
-    Tensor<int32_t, 1, 1> v89 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v88, 2);
-    Tensor<int32_t, 1, 1, 1> v90 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v89);
-    Tensor<int32_t, 1, 1> v91 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v90);
-    Tensor<float, 1, 1> v92 = emitc::tosa::cast<Tensor<float, 1, 1>>(v91);
-    Tensor<float, 1, 1> v93 = emitc::tosa::mul(v92, v40);
-    Tensor<float, 1, 1> v94 = emitc::tosa::mul(v93, v93);
-    Tensor<float, 1, 2> v95 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v93, v94);
-    Tensor<int64_t, 999> v96 = emitc::tosa::cast<Tensor<int64_t, 999>>(v72);
-    Tensor<int64_t, 1, 1, 1> v97 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v1);
-    Tensor<int64_t, 1, 1, 999> v98 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v96);
-    Tensor<int64_t, 1, 1, 999> v99 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v97, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v100 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v99, v98);
-    Tensor<int32_t, 1, 1, 999> v101 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v100);
-    Tensor<int32_t, 1, 1> v102 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v101, 2);
-    Tensor<int32_t, 1, 1, 1> v103 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v102);
-    Tensor<int32_t, 1, 1> v104 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v103);
-    Tensor<float, 1, 1> v105 = emitc::tosa::cast<Tensor<float, 1, 1>>(v104);
-    Tensor<float, 1, 1> v106 = emitc::tosa::mul(v105, v40);
-    Tensor<float, 1, 1> v107 = emitc::tosa::mul(v106, v106);
-    Tensor<float, 1, 2> v108 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v106, v107);
-    Tensor<int64_t, 999> v109 = emitc::tosa::cast<Tensor<int64_t, 999>>(v71);
-    Tensor<int64_t, 1, 1, 1> v110 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v34);
-    Tensor<int64_t, 1, 1, 999> v111 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v109);
-    Tensor<int64_t, 1, 1, 999> v112 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v110, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v113 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v112, v111);
-    Tensor<int32_t, 1, 1, 999> v114 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v113);
-    Tensor<int32_t, 1, 1> v115 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v114, 2);
-    Tensor<int32_t, 1, 1, 1> v116 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v115);
-    Tensor<int32_t, 1, 1> v117 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v116);
-    Tensor<float, 1, 1> v118 = emitc::tosa::cast<Tensor<float, 1, 1>>(v117);
-    Tensor<float, 1, 1> v119 = emitc::tosa::mul(v118, v40);
-    Tensor<float, 1, 1> v120 = emitc::tosa::mul(v119, v119);
-    Tensor<float, 1, 2> v121 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v119, v120);
-    Tensor<int64_t, 999> v122 = emitc::tosa::cast<Tensor<int64_t, 999>>(v70);
-    Tensor<int64_t, 1, 1, 1> v123 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v17);
-    Tensor<int64_t, 1, 1, 999> v124 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v122);
-    Tensor<int64_t, 1, 1, 999> v125 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v123, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v126 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v125, v124);
-    Tensor<int32_t, 1, 1, 999> v127 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v126);
-    Tensor<int32_t, 1, 1> v128 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v127, 2);
-    Tensor<int32_t, 1, 1, 1> v129 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v128);
-    Tensor<int32_t, 1, 1> v130 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v129);
-    Tensor<float, 1, 1> v131 = emitc::tosa::cast<Tensor<float, 1, 1>>(v130);
-    Tensor<float, 1, 1> v132 = emitc::tosa::mul(v131, v40);
-    Tensor<float, 1, 1> v133 = emitc::tosa::mul(v132, v132);
-    Tensor<float, 1, 2> v134 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v132, v133);
-    Tensor<int64_t, 1, 1, 1> v135 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v5);
-    Tensor<int64_t, 1, 1, 999> v136 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v135, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v137 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v136, v41);
-    Tensor<int32_t, 1, 1, 999> v138 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v137);
-    Tensor<int32_t, 1, 1> v139 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v138, 2);
-    Tensor<int32_t, 1, 1, 1> v140 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v139);
-    Tensor<int32_t, 1, 1> v141 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v140);
-    Tensor<float, 1, 1> v142 = emitc::tosa::cast<Tensor<float, 1, 1>>(v141);
-    Tensor<float, 1, 1> v143 = emitc::tosa::mul(v142, v40);
-    Tensor<float, 1, 1> v144 = emitc::tosa::mul(v143, v143);
-    Tensor<float, 1, 2> v145 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v143, v144);
-    Tensor<int64_t, 999> v146 = emitc::tosa::cast<Tensor<int64_t, 999>>(v57);
-    Tensor<int64_t, 1, 1, 1> v147 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v24);
-    Tensor<int64_t, 1, 1, 999> v148 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v146);
-    Tensor<int64_t, 1, 1, 999> v149 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v147, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v150 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v149, v148);
-    Tensor<int32_t, 1, 1, 999> v151 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v150);
-    Tensor<int32_t, 1, 1> v152 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v151, 2);
-    Tensor<int32_t, 1, 1, 1> v153 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v152);
-    Tensor<int32_t, 1, 1> v154 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v153);
-    Tensor<float, 1, 1> v155 = emitc::tosa::cast<Tensor<float, 1, 1>>(v154);
-    Tensor<float, 1, 1> v156 = emitc::tosa::mul(v155, v40);
-    Tensor<float, 1, 1> v157 = emitc::tosa::mul(v156, v156);
-    Tensor<float, 1, 2> v158 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v156, v157);
-    Tensor<int64_t, 999> v159 = emitc::tosa::cast<Tensor<int64_t, 999>>(v69);
-    Tensor<int64_t, 1, 1, 1> v160 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v35);
-    Tensor<int64_t, 1, 1, 999> v161 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v159);
-    Tensor<int64_t, 1, 1, 999> v162 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v160, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v163 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v162, v161);
-    Tensor<int32_t, 1, 1, 999> v164 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v163);
-    Tensor<int32_t, 1, 1> v165 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v164, 2);
-    Tensor<int32_t, 1, 1, 1> v166 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v165);
-    Tensor<int32_t, 1, 1> v167 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v166);
-    Tensor<float, 1, 1> v168 = emitc::tosa::cast<Tensor<float, 1, 1>>(v167);
-    Tensor<float, 1, 1> v169 = emitc::tosa::mul(v168, v40);
-    Tensor<float, 1, 1> v170 = emitc::tosa::mul(v169, v169);
-    Tensor<float, 1, 2> v171 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v169, v170);
-    Tensor<int64_t, 999> v172 = emitc::tosa::cast<Tensor<int64_t, 999>>(v68);
-    Tensor<int64_t, 1, 1, 1> v173 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v16);
-    Tensor<int64_t, 1, 1, 999> v174 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v172);
-    Tensor<int64_t, 1, 1, 999> v175 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v173, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v176 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v175, v174);
-    Tensor<int32_t, 1, 1, 999> v177 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v176);
-    Tensor<int32_t, 1, 1> v178 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v177, 2);
-    Tensor<int32_t, 1, 1, 1> v179 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v178);
-    Tensor<int32_t, 1, 1> v180 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v179);
-    Tensor<float, 1, 1> v181 = emitc::tosa::cast<Tensor<float, 1, 1>>(v180);
-    Tensor<float, 1, 1> v182 = emitc::tosa::mul(v181, v40);
-    Tensor<float, 1, 1> v183 = emitc::tosa::mul(v182, v182);
-    Tensor<float, 1, 2> v184 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v182, v183);
-    Tensor<int64_t, 999> v185 = emitc::tosa::cast<Tensor<int64_t, 999>>(v67);
-    Tensor<int64_t, 1, 1, 1> v186 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v23);
-    Tensor<int64_t, 1, 1, 999> v187 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v185);
-    Tensor<int64_t, 1, 1, 999> v188 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v186, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v189 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v188, v187);
-    Tensor<int32_t, 1, 1, 999> v190 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v189);
-    Tensor<int32_t, 1, 1> v191 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v190, 2);
-    Tensor<int32_t, 1, 1, 1> v192 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v191);
-    Tensor<int32_t, 1, 1> v193 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v192);
-    Tensor<float, 1, 1> v194 = emitc::tosa::cast<Tensor<float, 1, 1>>(v193);
-    Tensor<float, 1, 1> v195 = emitc::tosa::mul(v194, v40);
-    Tensor<float, 1, 1> v196 = emitc::tosa::mul(v195, v195);
-    Tensor<float, 1, 2> v197 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v195, v196);
-    Tensor<int64_t, 999> v198 = emitc::tosa::cast<Tensor<int64_t, 999>>(v66);
-    Tensor<int64_t, 1, 1, 1> v199 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v29);
-    Tensor<int64_t, 1, 1, 999> v200 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v198);
-    Tensor<int64_t, 1, 1, 999> v201 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v199, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v202 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v201, v200);
-    Tensor<int32_t, 1, 1, 999> v203 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v202);
-    Tensor<int32_t, 1, 1> v204 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v203, 2);
-    Tensor<int32_t, 1, 1, 1> v205 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v204);
-    Tensor<int32_t, 1, 1> v206 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v205);
-    Tensor<float, 1, 1> v207 = emitc::tosa::cast<Tensor<float, 1, 1>>(v206);
-    Tensor<float, 1, 1> v208 = emitc::tosa::mul(v207, v40);
-    Tensor<float, 1, 1> v209 = emitc::tosa::mul(v208, v208);
-    Tensor<float, 1, 2> v210 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v208, v209);
-    Tensor<int64_t, 1, 1, 1> v211 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v13);
-    Tensor<int64_t, 1, 1, 999> v212 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v211, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v213 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v212, v41);
-    Tensor<int32_t, 1, 1, 999> v214 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v213);
-    Tensor<int32_t, 1, 1> v215 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v214, 2);
-    Tensor<int32_t, 1, 1, 1> v216 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v215);
-    Tensor<int32_t, 1, 1> v217 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v216);
-    Tensor<float, 1, 1> v218 = emitc::tosa::cast<Tensor<float, 1, 1>>(v217);
-    Tensor<float, 1, 1> v219 = emitc::tosa::mul(v218, v40);
-    Tensor<float, 1, 1> v220 = emitc::tosa::mul(v219, v219);
-    Tensor<float, 1, 2> v221 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v219, v220);
-    Tensor<int64_t, 999> v222 = emitc::tosa::cast<Tensor<int64_t, 999>>(v65);
-    Tensor<int64_t, 1, 1, 1> v223 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v2);
-    Tensor<int64_t, 1, 1, 999> v224 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v222);
-    Tensor<int64_t, 1, 1, 999> v225 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v223, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v226 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v225, v224);
-    Tensor<int32_t, 1, 1, 999> v227 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v226);
-    Tensor<int32_t, 1, 1> v228 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v227, 2);
-    Tensor<int32_t, 1, 1, 1> v229 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v228);
-    Tensor<int32_t, 1, 1> v230 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v229);
-    Tensor<float, 1, 1> v231 = emitc::tosa::cast<Tensor<float, 1, 1>>(v230);
-    Tensor<float, 1, 1> v232 = emitc::tosa::mul(v231, v40);
-    Tensor<float, 1, 1> v233 = emitc::tosa::mul(v232, v232);
-    Tensor<float, 1, 2> v234 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v232, v233);
-    Tensor<int64_t, 999> v235 = emitc::tosa::cast<Tensor<int64_t, 999>>(v64);
-    Tensor<int64_t, 1, 1, 1> v236 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v11);
-    Tensor<int64_t, 1, 1, 999> v237 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v235);
-    Tensor<int64_t, 1, 1, 999> v238 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v236, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v239 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v238, v237);
-    Tensor<int32_t, 1, 1, 999> v240 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v239);
-    Tensor<int32_t, 1, 1> v241 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v240, 2);
-    Tensor<int32_t, 1, 1, 1> v242 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v241);
-    Tensor<int32_t, 1, 1> v243 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v242);
-    Tensor<float, 1, 1> v244 = emitc::tosa::cast<Tensor<float, 1, 1>>(v243);
-    Tensor<float, 1, 1> v245 = emitc::tosa::mul(v244, v40);
-    Tensor<float, 1, 1> v246 = emitc::tosa::mul(v245, v245);
-    Tensor<float, 1, 2> v247 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v245, v246);
-    Tensor<int64_t, 1, 1, 1> v248 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v32);
-    Tensor<int64_t, 1, 1, 999> v249 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v248, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v250 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v249, v41);
-    Tensor<int32_t, 1, 1, 999> v251 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v250);
-    Tensor<int32_t, 1, 1> v252 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v251, 2);
-    Tensor<int32_t, 1, 1, 1> v253 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v252);
-    Tensor<int32_t, 1, 1> v254 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v253);
-    Tensor<float, 1, 1> v255 = emitc::tosa::cast<Tensor<float, 1, 1>>(v254);
-    Tensor<float, 1, 1> v256 = emitc::tosa::mul(v255, v40);
-    Tensor<float, 1, 1> v257 = emitc::tosa::mul(v256, v256);
-    Tensor<float, 1, 2> v258 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v256, v257);
-    Tensor<int64_t, 999> v259 = emitc::tosa::cast<Tensor<int64_t, 999>>(v63);
-    Tensor<int64_t, 1, 1, 1> v260 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v14);
-    Tensor<int64_t, 1, 1, 999> v261 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v259);
-    Tensor<int64_t, 1, 1, 999> v262 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v260, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v263 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v262, v261);
-    Tensor<int32_t, 1, 1, 999> v264 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v263);
-    Tensor<int32_t, 1, 1> v265 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v264, 2);
-    Tensor<int32_t, 1, 1, 1> v266 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v265);
-    Tensor<int32_t, 1, 1> v267 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v266);
-    Tensor<float, 1, 1> v268 = emitc::tosa::cast<Tensor<float, 1, 1>>(v267);
-    Tensor<float, 1, 1> v269 = emitc::tosa::mul(v268, v40);
-    Tensor<float, 1, 1> v270 = emitc::tosa::mul(v269, v269);
-    Tensor<float, 1, 2> v271 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v269, v270);
-    Tensor<int64_t, 1, 1, 1> v272 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v10);
-    Tensor<int64_t, 1, 1, 999> v273 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v272, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v274 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v273, v41);
-    Tensor<int32_t, 1, 1, 999> v275 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v274);
-    Tensor<int32_t, 1, 1> v276 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v275, 2);
-    Tensor<int32_t, 1, 1, 1> v277 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v276);
-    Tensor<int32_t, 1, 1> v278 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v277);
-    Tensor<float, 1, 1> v279 = emitc::tosa::cast<Tensor<float, 1, 1>>(v278);
-    Tensor<float, 1, 1> v280 = emitc::tosa::mul(v279, v40);
-    Tensor<float, 1, 1> v281 = emitc::tosa::mul(v280, v280);
-    Tensor<float, 1, 2> v282 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v280, v281);
-    Tensor<int64_t, 999> v283 = emitc::tosa::cast<Tensor<int64_t, 999>>(v62);
-    Tensor<int64_t, 1, 1, 1> v284 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v21);
-    Tensor<int64_t, 1, 1, 999> v285 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v283);
-    Tensor<int64_t, 1, 1, 999> v286 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v284, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v287 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v286, v285);
-    Tensor<int32_t, 1, 1, 999> v288 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v287);
-    Tensor<int32_t, 1, 1> v289 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v288, 2);
-    Tensor<int32_t, 1, 1, 1> v290 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v289);
-    Tensor<int32_t, 1, 1> v291 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v290);
-    Tensor<float, 1, 1> v292 = emitc::tosa::cast<Tensor<float, 1, 1>>(v291);
-    Tensor<float, 1, 1> v293 = emitc::tosa::mul(v292, v40);
-    Tensor<float, 1, 1> v294 = emitc::tosa::mul(v293, v293);
-    Tensor<float, 1, 2> v295 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v293, v294);
-    Tensor<int64_t, 9999> v296 = emitc::tosa::cast<Tensor<int64_t, 9999>>(v61);
-    Tensor<int64_t, 1, 1, 1> v297 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v33);
-    Tensor<int64_t, 1, 1, 9999> v298 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 9999>>(v296);
-    Tensor<int64_t, 1, 1, 9999> v299 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 9999>>(v297, {0, 1, 2});
-    Tensor<bool, 1, 1, 9999> v300 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 9999>>(v299, v298);
-    Tensor<int32_t, 1, 1, 9999> v301 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 9999>>(v300);
-    Tensor<int32_t, 1, 1> v302 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 9999>>(v301, 2);
-    Tensor<int32_t, 1, 1, 1> v303 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v302);
-    Tensor<int32_t, 1, 1> v304 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v303);
-    Tensor<float, 1, 1> v305 = emitc::tosa::cast<Tensor<float, 1, 1>>(v304);
-    Tensor<float, 1, 1> v306 = emitc::tosa::mul(v305, v42);
-    Tensor<float, 1, 1> v307 = emitc::tosa::mul(v306, v306);
-    Tensor<float, 1, 2> v308 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v306, v307);
-    Tensor<int64_t, 9999> v309 = emitc::tosa::cast<Tensor<int64_t, 9999>>(v60);
-    Tensor<int64_t, 1, 1, 1> v310 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v30);
-    Tensor<int64_t, 1, 1, 9999> v311 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 9999>>(v309);
-    Tensor<int64_t, 1, 1, 9999> v312 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 9999>>(v310, {0, 1, 2});
-    Tensor<bool, 1, 1, 9999> v313 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 9999>>(v312, v311);
-    Tensor<int32_t, 1, 1, 9999> v314 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 9999>>(v313);
-    Tensor<int32_t, 1, 1> v315 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 9999>>(v314, 2);
-    Tensor<int32_t, 1, 1, 1> v316 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v315);
-    Tensor<int32_t, 1, 1> v317 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v316);
-    Tensor<float, 1, 1> v318 = emitc::tosa::cast<Tensor<float, 1, 1>>(v317);
-    Tensor<float, 1, 1> v319 = emitc::tosa::mul(v318, v42);
-    Tensor<float, 1, 1> v320 = emitc::tosa::mul(v319, v319);
-    Tensor<float, 1, 2> v321 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v319, v320);
-    Tensor<int64_t, 999> v322 = emitc::tosa::cast<Tensor<int64_t, 999>>(v59);
-    Tensor<int64_t, 1, 1, 1> v323 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v19);
-    Tensor<int64_t, 1, 1, 999> v324 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v322);
-    Tensor<int64_t, 1, 1, 999> v325 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v323, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v326 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v325, v324);
-    Tensor<int32_t, 1, 1, 999> v327 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v326);
-    Tensor<int32_t, 1, 1> v328 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v327, 2);
-    Tensor<int32_t, 1, 1, 1> v329 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v328);
-    Tensor<int32_t, 1, 1> v330 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v329);
-    Tensor<float, 1, 1> v331 = emitc::tosa::cast<Tensor<float, 1, 1>>(v330);
-    Tensor<float, 1, 1> v332 = emitc::tosa::mul(v331, v40);
-    Tensor<float, 1, 1> v333 = emitc::tosa::mul(v332, v332);
-    Tensor<float, 1, 2> v334 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v332, v333);
-    Tensor<int64_t, 999> v335 = emitc::tosa::cast<Tensor<int64_t, 999>>(v58);
-    Tensor<int64_t, 1, 1, 1> v336 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v18);
-    Tensor<int64_t, 1, 1, 999> v337 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v335);
-    Tensor<int64_t, 1, 1, 999> v338 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v336, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v339 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v338, v337);
-    Tensor<int32_t, 1, 1, 999> v340 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v339);
-    Tensor<int32_t, 1, 1> v341 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v340, 2);
-    Tensor<int32_t, 1, 1, 1> v342 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v341);
-    Tensor<int32_t, 1, 1> v343 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v342);
-    Tensor<float, 1, 1> v344 = emitc::tosa::cast<Tensor<float, 1, 1>>(v343);
-    Tensor<float, 1, 1> v345 = emitc::tosa::mul(v344, v40);
-    Tensor<float, 1, 1> v346 = emitc::tosa::mul(v345, v345);
-    Tensor<float, 1, 2> v347 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v345, v346);
-    Tensor<int64_t, 999> v348 = emitc::tosa::cast<Tensor<int64_t, 999>>(v57);
-    Tensor<int64_t, 1, 1, 1> v349 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v9);
-    Tensor<int64_t, 1, 1, 999> v350 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v348);
-    Tensor<int64_t, 1, 1, 999> v351 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v349, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v352 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v351, v350);
-    Tensor<int32_t, 1, 1, 999> v353 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v352);
-    Tensor<int32_t, 1, 1> v354 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v353, 2);
-    Tensor<int32_t, 1, 1, 1> v355 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v354);
-    Tensor<int32_t, 1, 1> v356 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v355);
-    Tensor<float, 1, 1> v357 = emitc::tosa::cast<Tensor<float, 1, 1>>(v356);
-    Tensor<float, 1, 1> v358 = emitc::tosa::mul(v357, v40);
-    Tensor<float, 1, 1> v359 = emitc::tosa::mul(v358, v358);
-    Tensor<float, 1, 2> v360 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v358, v359);
-    Tensor<int64_t, 999> v361 = emitc::tosa::cast<Tensor<int64_t, 999>>(v56);
-    Tensor<int64_t, 1, 1, 1> v362 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v27);
-    Tensor<int64_t, 1, 1, 999> v363 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v361);
-    Tensor<int64_t, 1, 1, 999> v364 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v362, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v365 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v364, v363);
-    Tensor<int32_t, 1, 1, 999> v366 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v365);
-    Tensor<int32_t, 1, 1> v367 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v366, 2);
-    Tensor<int32_t, 1, 1, 1> v368 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v367);
-    Tensor<int32_t, 1, 1> v369 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v368);
-    Tensor<float, 1, 1> v370 = emitc::tosa::cast<Tensor<float, 1, 1>>(v369);
-    Tensor<float, 1, 1> v371 = emitc::tosa::mul(v370, v40);
-    Tensor<float, 1, 1> v372 = emitc::tosa::mul(v371, v371);
-    Tensor<float, 1, 2> v373 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v371, v372);
-    Tensor<int64_t, 999> v374 = emitc::tosa::cast<Tensor<int64_t, 999>>(v55);
-    Tensor<int64_t, 1, 1, 1> v375 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v26);
-    Tensor<int64_t, 1, 1, 999> v376 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v374);
-    Tensor<int64_t, 1, 1, 999> v377 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v375, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v378 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v377, v376);
-    Tensor<int32_t, 1, 1, 999> v379 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v378);
-    Tensor<int32_t, 1, 1> v380 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v379, 2);
-    Tensor<int32_t, 1, 1, 1> v381 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v380);
-    Tensor<int32_t, 1, 1> v382 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v381);
-    Tensor<float, 1, 1> v383 = emitc::tosa::cast<Tensor<float, 1, 1>>(v382);
-    Tensor<float, 1, 1> v384 = emitc::tosa::mul(v383, v40);
-    Tensor<float, 1, 1> v385 = emitc::tosa::mul(v384, v384);
-    Tensor<float, 1, 2> v386 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v384, v385);
-    Tensor<int64_t, 999> v387 = emitc::tosa::cast<Tensor<int64_t, 999>>(v54);
-    Tensor<int64_t, 1, 1, 1> v388 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v25);
-    Tensor<int64_t, 1, 1, 999> v389 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v387);
-    Tensor<int64_t, 1, 1, 999> v390 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v388, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v391 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v390, v389);
-    Tensor<int32_t, 1, 1, 999> v392 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v391);
-    Tensor<int32_t, 1, 1> v393 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v392, 2);
-    Tensor<int32_t, 1, 1, 1> v394 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v393);
-    Tensor<int32_t, 1, 1> v395 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v394);
-    Tensor<float, 1, 1> v396 = emitc::tosa::cast<Tensor<float, 1, 1>>(v395);
-    Tensor<float, 1, 1> v397 = emitc::tosa::mul(v396, v40);
-    Tensor<float, 1, 1> v398 = emitc::tosa::mul(v397, v397);
-    Tensor<float, 1, 2> v399 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v397, v398);
-    Tensor<int64_t, 999> v400 = emitc::tosa::cast<Tensor<int64_t, 999>>(v53);
-    Tensor<int64_t, 1, 1, 1> v401 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v20);
-    Tensor<int64_t, 1, 1, 999> v402 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v400);
-    Tensor<int64_t, 1, 1, 999> v403 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v401, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v404 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v403, v402);
-    Tensor<int32_t, 1, 1, 999> v405 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v404);
-    Tensor<int32_t, 1, 1> v406 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v405, 2);
-    Tensor<int32_t, 1, 1, 1> v407 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v406);
-    Tensor<int32_t, 1, 1> v408 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v407);
-    Tensor<float, 1, 1> v409 = emitc::tosa::cast<Tensor<float, 1, 1>>(v408);
-    Tensor<float, 1, 1> v410 = emitc::tosa::mul(v409, v40);
-    Tensor<float, 1, 1> v411 = emitc::tosa::mul(v410, v410);
-    Tensor<float, 1, 2> v412 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v410, v411);
-    Tensor<int64_t, 1, 1, 1> v413 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v36);
-    Tensor<int64_t, 1, 1, 999> v414 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v413, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v415 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v414, v41);
-    Tensor<int32_t, 1, 1, 999> v416 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v415);
-    Tensor<int32_t, 1, 1> v417 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v416, 2);
-    Tensor<int32_t, 1, 1, 1> v418 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v417);
-    Tensor<int32_t, 1, 1> v419 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v418);
-    Tensor<float, 1, 1> v420 = emitc::tosa::cast<Tensor<float, 1, 1>>(v419);
-    Tensor<float, 1, 1> v421 = emitc::tosa::mul(v420, v40);
-    Tensor<float, 1, 1> v422 = emitc::tosa::mul(v421, v421);
-    Tensor<float, 1, 2> v423 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v421, v422);
-    Tensor<int64_t, 999> v424 = emitc::tosa::cast<Tensor<int64_t, 999>>(v52);
-    Tensor<int64_t, 1, 1, 1> v425 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v22);
-    Tensor<int64_t, 1, 1, 999> v426 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v424);
-    Tensor<int64_t, 1, 1, 999> v427 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v425, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v428 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v427, v426);
-    Tensor<int32_t, 1, 1, 999> v429 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v428);
-    Tensor<int32_t, 1, 1> v430 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v429, 2);
-    Tensor<int32_t, 1, 1, 1> v431 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v430);
-    Tensor<int32_t, 1, 1> v432 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v431);
-    Tensor<float, 1, 1> v433 = emitc::tosa::cast<Tensor<float, 1, 1>>(v432);
-    Tensor<float, 1, 1> v434 = emitc::tosa::mul(v433, v40);
-    Tensor<float, 1, 1> v435 = emitc::tosa::mul(v434, v434);
-    Tensor<float, 1, 2> v436 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v434, v435);
-    Tensor<int64_t, 999> v437 = emitc::tosa::cast<Tensor<int64_t, 999>>(v51);
-    Tensor<int64_t, 1, 1, 1> v438 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v12);
-    Tensor<int64_t, 1, 1, 999> v439 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v437);
-    Tensor<int64_t, 1, 1, 999> v440 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v438, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v441 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v440, v439);
-    Tensor<int32_t, 1, 1, 999> v442 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v441);
-    Tensor<int32_t, 1, 1> v443 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v442, 2);
-    Tensor<int32_t, 1, 1, 1> v444 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v443);
-    Tensor<int32_t, 1, 1> v445 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v444);
-    Tensor<float, 1, 1> v446 = emitc::tosa::cast<Tensor<float, 1, 1>>(v445);
-    Tensor<float, 1, 1> v447 = emitc::tosa::mul(v446, v40);
-    Tensor<float, 1, 1> v448 = emitc::tosa::mul(v447, v447);
-    Tensor<float, 1, 2> v449 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v447, v448);
-    Tensor<int64_t, 999> v450 = emitc::tosa::cast<Tensor<int64_t, 999>>(v50);
-    Tensor<int64_t, 1, 1, 1> v451 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v15);
-    Tensor<int64_t, 1, 1, 999> v452 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v450);
-    Tensor<int64_t, 1, 1, 999> v453 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v451, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v454 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v453, v452);
-    Tensor<int32_t, 1, 1, 999> v455 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v454);
-    Tensor<int32_t, 1, 1> v456 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v455, 2);
-    Tensor<int32_t, 1, 1, 1> v457 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v456);
-    Tensor<int32_t, 1, 1> v458 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v457);
-    Tensor<float, 1, 1> v459 = emitc::tosa::cast<Tensor<float, 1, 1>>(v458);
-    Tensor<float, 1, 1> v460 = emitc::tosa::mul(v459, v40);
-    Tensor<float, 1, 1> v461 = emitc::tosa::mul(v460, v460);
-    Tensor<float, 1, 2> v462 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v460, v461);
-    Tensor<int64_t, 999> v463 = emitc::tosa::cast<Tensor<int64_t, 999>>(v49);
-    Tensor<int64_t, 1, 1, 1> v464 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v8);
-    Tensor<int64_t, 1, 1, 999> v465 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v463);
-    Tensor<int64_t, 1, 1, 999> v466 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v464, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v467 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v466, v465);
-    Tensor<int32_t, 1, 1, 999> v468 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v467);
-    Tensor<int32_t, 1, 1> v469 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v468, 2);
-    Tensor<int32_t, 1, 1, 1> v470 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v469);
-    Tensor<int32_t, 1, 1> v471 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v470);
-    Tensor<float, 1, 1> v472 = emitc::tosa::cast<Tensor<float, 1, 1>>(v471);
-    Tensor<float, 1, 1> v473 = emitc::tosa::mul(v472, v40);
-    Tensor<float, 1, 1> v474 = emitc::tosa::mul(v473, v473);
-    Tensor<float, 1, 2> v475 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v473, v474);
-    Tensor<int64_t, 999> v476 = emitc::tosa::cast<Tensor<int64_t, 999>>(v48);
-    Tensor<int64_t, 1, 1, 1> v477 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v6);
-    Tensor<int64_t, 1, 1, 999> v478 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v476);
-    Tensor<int64_t, 1, 1, 999> v479 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v477, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v480 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v479, v478);
-    Tensor<int32_t, 1, 1, 999> v481 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v480);
-    Tensor<int32_t, 1, 1> v482 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v481, 2);
-    Tensor<int32_t, 1, 1, 1> v483 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v482);
-    Tensor<int32_t, 1, 1> v484 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v483);
-    Tensor<float, 1, 1> v485 = emitc::tosa::cast<Tensor<float, 1, 1>>(v484);
-    Tensor<float, 1, 1> v486 = emitc::tosa::mul(v485, v40);
-    Tensor<float, 1, 1> v487 = emitc::tosa::mul(v486, v486);
-    Tensor<float, 1, 2> v488 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v486, v487);
-    Tensor<int64_t, 999> v489 = emitc::tosa::cast<Tensor<int64_t, 999>>(v47);
-    Tensor<int64_t, 1, 1, 1> v490 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v7);
-    Tensor<int64_t, 1, 1, 999> v491 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v489);
-    Tensor<int64_t, 1, 1, 999> v492 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v490, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v493 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v492, v491);
-    Tensor<int32_t, 1, 1, 999> v494 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v493);
-    Tensor<int32_t, 1, 1> v495 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v494, 2);
-    Tensor<int32_t, 1, 1, 1> v496 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v495);
-    Tensor<int32_t, 1, 1> v497 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v496);
-    Tensor<float, 1, 1> v498 = emitc::tosa::cast<Tensor<float, 1, 1>>(v497);
-    Tensor<float, 1, 1> v499 = emitc::tosa::mul(v498, v40);
-    Tensor<float, 1, 1> v500 = emitc::tosa::mul(v499, v499);
-    Tensor<float, 1, 2> v501 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v499, v500);
-    Tensor<int64_t, 999> v502 = emitc::tosa::cast<Tensor<int64_t, 999>>(v46);
-    Tensor<int64_t, 1, 1, 1> v503 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v31);
-    Tensor<int64_t, 1, 1, 999> v504 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v502);
-    Tensor<int64_t, 1, 1, 999> v505 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v503, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v506 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v505, v504);
-    Tensor<int32_t, 1, 1, 999> v507 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v506);
-    Tensor<int32_t, 1, 1> v508 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v507, 2);
-    Tensor<int32_t, 1, 1, 1> v509 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v508);
-    Tensor<int32_t, 1, 1> v510 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v509);
-    Tensor<float, 1, 1> v511 = emitc::tosa::cast<Tensor<float, 1, 1>>(v510);
-    Tensor<float, 1, 1> v512 = emitc::tosa::mul(v511, v40);
-    Tensor<float, 1, 1> v513 = emitc::tosa::mul(v512, v512);
-    Tensor<float, 1, 2> v514 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v512, v513);
-    Tensor<int64_t, 999> v515 = emitc::tosa::cast<Tensor<int64_t, 999>>(v45);
-    Tensor<int64_t, 1, 1, 1> v516 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v3);
-    Tensor<int64_t, 1, 1, 999> v517 =
-        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v515);
-    Tensor<int64_t, 1, 1, 999> v518 =
-        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v516, {0, 1, 2});
-    Tensor<bool, 1, 1, 999> v519 =
-        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v518, v517);
-    Tensor<int32_t, 1, 1, 999> v520 =
-        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v519);
-    Tensor<int32_t, 1, 1> v521 =
-        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
-                                Tensor<int32_t, 1, 1, 999>>(v520, 2);
-    Tensor<int32_t, 1, 1, 1> v522 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v521);
-    Tensor<int32_t, 1, 1> v523 =
-        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v522);
-    Tensor<float, 1, 1> v524 = emitc::tosa::cast<Tensor<float, 1, 1>>(v523);
-    Tensor<float, 1, 1> v525 = emitc::tosa::mul(v524, v40);
-    Tensor<float, 1, 1> v526 = emitc::tosa::mul(v525, v525);
-    Tensor<float, 1, 2> v527 =
-        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v525, v526);
-    Tensor<float, 1, 71> v528 = emitc::tosa::concat<1, Tensor<float, 1, 71>>(
-        v334, v462, v475, v488, v501, v514, v527, v95, v108, v121, v134, v145,
-        v158, v171, v184, v197, v210, v221, v74, v234, v247, v258, v271, v282,
-        v295, v308, v321, v347, v360, v373, v386, v399, v412, v423, v436, v449);
-    Tensor<float, 1, 40> v529 =
-        emitc::tosa::fully_connected<Tensor<float, 1, 40>>(v528, v75, v76);
-    Tensor<float, 1, 40> v530 =
-        emitc::tosa::clamp(v529, (float)0.0e+00, (float)3.402823470e+38);
-    Tensor<float, 1, 40> v531 =
-        emitc::tosa::fully_connected<Tensor<float, 1, 40>>(v530, v77, v78);
-    Tensor<float, 1, 40> v532 =
-        emitc::tosa::clamp(v531, (float)0.0e+00, (float)3.402823470e+38);
-    Tensor<float, 1, 20> v533 =
-        emitc::tosa::fully_connected<Tensor<float, 1, 20>>(v532, v79, v80);
-    Tensor<float, 1, 20> v534 =
-        emitc::tosa::clamp(v533, (float)0.0e+00, (float)3.402823470e+38);
-    Tensor<float, 1, 2> v535 =
-        emitc::tosa::fully_connected<Tensor<float, 1, 2>>(v534, v81, v82);
-    Tensor<int64_t, 1> v536 =
-        emitc::tosa::argmax<Tensor<int64_t, 1>, Tensor<float, 1, 2>>(v535, 1);
-    Tensor<int64_t, 1> v537 = emitc::tosa::minimum(v536, v43);
-    Tensor<int64_t, 1> v538 = emitc::tosa::maximum(v537, v44);
-    return v538;
-  }
-};
+int foo();
diff --git a/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.cpp b/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.cpp
new file mode 100644
--- /dev/null
+++ b/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.cpp
@@ -0,0 +1,19829 @@
+#include "llvm/Analysis/EmitCModelRegistry.h"
+#include "llvm/Analysis/EmitCTensor.h"
+#include "llvm/Analysis/MLInlineEmitCModel.h"
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <cmath>
+#include <complex>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <functional>
+#include <limits>
+#include <numeric>
+#include <type_traits>
+#include <vector>
+
+#include "llvm/Support/CommandLine.h"
+
+namespace {
+namespace emitc {
+namespace utility {
+template <size_t... Shape> static constexpr size_t size() {
+  constexpr std::array<size_t, sizeof...(Shape)> s = {Shape...};
+  size_t result = 1;
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    result *= s[i];
+  }
+  return result;
+}
+template <size_t... Shape>
+static constexpr std::array<size_t, sizeof...(Shape)> strides() {
+  std::array<size_t, sizeof...(Shape)> result = {};
+  constexpr std::array<size_t, sizeof...(Shape)> s = {Shape...};
+  if (sizeof...(Shape) == 0) {
+    return result;
+  }
+  result[sizeof...(Shape) - 1] = 1;
+  for (size_t i = sizeof...(Shape) - 1; i > 0; i--) {
+    result[i - 1] = result[i] * s[i];
+  }
+  return result;
+}
+template <size_t... Shape>
+constexpr size_t ravel_index(std::array<size_t, sizeof...(Shape)> indices) {
+  std::array<size_t, sizeof...(Shape)> shape = {Shape...};
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    assert(indices[i] < shape[i]);
+  }
+  std::array<size_t, sizeof...(Shape)> s = strides<Shape...>();
+  size_t result = 0;
+  for (size_t i = 0; i < indices.size(); ++i) {
+    result += indices[i] * s[i];
+  }
+  return result;
+}
+template <size_t... Shape, typename... Indices>
+constexpr size_t ravel_index(Indices... indices) {
+  static_assert(sizeof...(Indices) == sizeof...(Shape),
+                "Incorrect number of arguments");
+  return ravel_index<Shape...>({static_cast<size_t>(indices)...});
+}
+template <size_t... Shape>
+constexpr std::array<size_t, sizeof...(Shape)> unravel_index(size_t index) {
+  assert(index < size<Shape...>());
+  std::array<size_t, sizeof...(Shape)> s = strides<Shape...>();
+  std::array<size_t, sizeof...(Shape)> result = {};
+  for (size_t i = 0; i < sizeof...(Shape); ++i) {
+    result[i] = index / s[i];
+    index = index % s[i];
+  }
+  return result;
+}
+} // namespace utility
+} // namespace emitc
+namespace detail {
+template <size_t N> constexpr size_t sum(const std::array<size_t, N> arr) {
+  size_t result = 0;
+  for (size_t i = 0; i < arr.size(); ++i) {
+    result += arr[i];
+  }
+  return result;
+}
+template <size_t N> constexpr size_t first(const std::array<size_t, N> arr) {
+  static_assert(N > 0, "Cannot get the first element of an empty array");
+  return arr[0];
+}
+template <size_t N> constexpr bool all_same(const std::array<size_t, N> arr) {
+  if (arr.size() == 0) {
+    return true;
+  }
+  size_t first = arr[0];
+  for (size_t i = 1; i < arr.size(); ++i) {
+    if (arr[i] != first) {
+      return false;
+    }
+  }
+  return true;
+}
+template <class...> struct conjunction : std::true_type {};
+template <class B1> struct conjunction<B1> : B1 {};
+template <class B1, class... Bn>
+struct conjunction<B1, Bn...>
+    : std::conditional_t<bool(B1::value), conjunction<Bn...>, B1> {};
+template <class... B> constexpr bool conjunction_v = conjunction<B...>::value;
+template <bool B, typename T> struct case_t {
+  static constexpr bool value = B;
+  using type = T;
+};
+template <typename First, typename... Rest>
+struct switch_t : std::conditional_t<First::value, First, switch_t<Rest...>> {};
+template <typename T> struct switch_t<T> {
+  using type = T;
+};
+template <bool B, typename T> struct switch_t<case_t<B, T>> {
+  static_assert(B, "None of the supplied conditions evaluate to true.");
+  using type = T;
+};
+} // namespace detail
+template <typename T> using Tensor0D = llvm::emitc::Tensor<T>;
+template <typename T, size_t Dim0> using Tensor1D = llvm::emitc::Tensor<T, Dim0>;
+template <typename T, size_t Dim0, size_t Dim1>
+using Tensor2D = llvm::emitc::Tensor<T, Dim0, Dim1>;
+template <typename T, size_t Dim0, size_t Dim1, size_t Dim2>
+using Tensor3D = llvm::emitc::Tensor<T, Dim0, Dim1, Dim2>;
+template <typename T, size_t Dim0, size_t Dim1, size_t Dim2, size_t Dim3>
+using Tensor4D = llvm::emitc::Tensor<T, Dim0, Dim1, Dim2, Dim3>;
+template <typename T> using is_scalar = std::is_arithmetic<T>;
+template <typename T, typename Unused = void>
+struct is_tensor : std::false_type {};
+template <typename T, size_t... Shape>
+struct is_tensor<llvm::emitc::Tensor<T, Shape...>> : std::true_type {};
+template <size_t Dim, typename T, typename Unused = void>
+struct is_tensor_of_dim : std::false_type {};
+template <size_t Dim, typename T, size_t... Shape>
+struct is_tensor_of_dim<Dim, llvm::emitc::Tensor<T, Shape...>> {
+  static constexpr bool value = llvm::emitc::Tensor<T, Shape...>::rank() == Dim;
+};
+template <typename T>
+using IsScalar = typename std::enable_if_t<std::is_scalar<T>::value, bool>;
+template <typename T>
+using IsTensor = typename std::enable_if_t<is_tensor<T>::value, bool>;
+template <size_t Dim, typename T>
+using IsTensorOfDim =
+    typename std::enable_if_t<is_tensor_of_dim<Dim, T>::value, bool>;
+template <typename T> struct get_element_type {
+  using type = T;
+};
+template <typename T, size_t... Shape>
+struct get_element_type<llvm::emitc::Tensor<T, Shape...>> {
+  using type = T;
+};
+template <typename T, typename ET>
+using IsTensorOfType = std::enable_if_t<
+    std::is_same<typename get_element_type<T>::type, ET>::value, bool>;
+template <typename Dest, typename Src> struct replace_element_type {
+  using type = Dest;
+};
+template <typename Dest, typename Src, size_t... Shape>
+struct replace_element_type<Dest, llvm::emitc::Tensor<Src, Shape...>> {
+  using type = llvm::emitc::Tensor<Dest, Shape...>;
+};
+template <typename Dest, typename Src> using UnaryFuncType = Dest (*)(Src);
+template <typename Dest, typename SrcLeft, typename SrcRight>
+using BinaryFuncType = Dest (*)(SrcLeft, SrcRight);
+template <typename Dest, typename Src, typename UnaryOp, IsScalar<Src> = true>
+inline Dest unary(const Src &x, UnaryOp &&op) {
+  return op(x);
+}
+template <typename Dest, typename Src, typename UnaryOp, IsTensor<Src> = true>
+inline Dest unary(const Src &x, UnaryOp &&op) {
+  Dest z;
+  std::transform(x.begin(), x.end(), z.begin(), op);
+  return z;
+}
+template <typename Dest, typename SrcLeft, typename SrcRight, typename BinaryOp,
+          IsScalar<SrcLeft> = true, IsScalar<SrcRight> = true>
+inline Dest binary(const SrcLeft &x, const SrcRight &y, BinaryOp &&op) {
+  return op(x, y);
+}
+template <typename Dest, typename SrcLeft, typename SrcRight, typename BinaryOp,
+          IsTensor<SrcLeft> = true, IsTensor<SrcRight> = true>
+inline Dest binary(const SrcLeft &x, const SrcRight &y, BinaryOp &&op) {
+  Dest z;
+  std::transform(x.begin(), x.end(), y.begin(), z.begin(), op);
+  return z;
+}
+template <typename Dest, typename SrcA, typename SrcB, typename SrcC,
+          typename TernaryOp, IsScalar<SrcA> = true, IsScalar<SrcB> = true,
+          IsScalar<SrcC> = true>
+inline Dest ternary(const SrcA &a, const SrcB &b, const SrcB &c,
+                    TernaryOp &&op) {
+  return op(a, b, c);
+}
+template <typename Dest, typename SrcA, typename SrcB, typename SrcC,
+          typename TernaryOp, IsTensor<SrcA> = true, IsTensor<SrcB> = true,
+          IsTensor<SrcC> = true>
+inline Dest ternary(const SrcA &a, const SrcB &b, const SrcB &c,
+                    TernaryOp &&op) {
+  Dest d;
+  auto first1 = a.begin(), last1 = a.end();
+  auto first2 = b.begin(), first3 = c.begin();
+  auto result = d.begin();
+  while (first1 != last1) {
+    *result = op(*first1, *first2, *first3);
+    ++result;
+    ++first1;
+    ++first2;
+    ++first3;
+  }
+  return d;
+}
+template <size_t Dim, typename T, typename... Ts> struct concat {};
+template <size_t Dim, typename T, size_t... Xs>
+struct concat<Dim, T, Tensor1D<T, Xs>...> {
+  static_assert(0 <= Dim && Dim < 1, "Dimension index out of bounds");
+  using type = Tensor1D<T, detail::sum<sizeof...(Xs)>({Xs...})>;
+};
+template <typename T, size_t Dim, size_t... Xs, size_t... Ys>
+struct concat<Dim, T, Tensor2D<T, Xs, Ys>...> {
+  static_assert(0 <= Dim && Dim < 2, "Dimension index out of bounds");
+  static_assert((Dim == 0 && detail::all_same<sizeof...(Ys)>({Ys...})) ||
+                    (Dim == 1 && detail::all_same<sizeof...(Xs)>({Xs...})),
+                "All dimensions except for the dimension index must match");
+  using type = typename std::conditional_t<
+      Dim == 0,
+      Tensor2D<T, detail::sum<sizeof...(Xs)>({Xs...}),
+               detail::first<sizeof...(Ys)>({Ys...})>,
+      Tensor2D<T, detail::first<sizeof...(Xs)>({Xs...}),
+               detail::sum<sizeof...(Ys)>({Ys...})>>;
+};
+template <typename T, size_t Dim, size_t... Xs, size_t... Ys, size_t... Zs>
+struct concat<Dim, T, Tensor3D<T, Xs, Ys, Zs>...> {
+  static_assert(0 <= Dim && Dim < 3, "Dimension index out of bounds");
+  using type = typename detail::switch_t<
+      detail::case_t<Dim == 0, Tensor3D<T, detail::sum<sizeof...(Xs)>({Xs...}),
+                                        detail::first<sizeof...(Ys)>({Ys...}),
+                                        detail::first<sizeof...(Zs)>({Zs...})>>,
+      detail::case_t<Dim == 1,
+                     Tensor3D<T, detail::first<sizeof...(Xs)>({Xs...}),
+                              detail::sum<sizeof...(Ys)>({Ys...}),
+                              detail::first<sizeof...(Zs)>({Zs...})>>,
+      detail::case_t<Dim == 2,
+                     Tensor3D<T, detail::first<sizeof...(Xs)>({Xs...}),
+                              detail::first<sizeof...(Ys)>({Ys...}),
+                              detail::sum<sizeof...(Zs)>({Zs...})>>>::type;
+};
+template <typename T, size_t Dim, size_t... D0, size_t... D1, size_t... D2,
+          size_t... D3>
+struct concat<Dim, T, Tensor4D<T, D0, D1, D2, D3>...> {
+  static_assert(0 <= Dim && Dim < 4, "Dimension index out of bounds");
+  using type = typename detail::switch_t<
+      detail::case_t<Dim == 0, Tensor4D<T, detail::sum<sizeof...(D0)>({D0...}),
+                                        detail::first<sizeof...(D1)>({D1...}),
+                                        detail::first<sizeof...(D2)>({D2...}),
+                                        detail::first<sizeof...(D3)>({D3...})>>,
+      detail::case_t<Dim == 1,
+                     Tensor4D<T, detail::first<sizeof...(D0)>({D0...}),
+                              detail::sum<sizeof...(D1)>({D1...}),
+                              detail::first<sizeof...(D2)>({D2...}),
+                              detail::first<sizeof...(D3)>({D3...})>>,
+      detail::case_t<Dim == 2,
+                     Tensor4D<T, detail::first<sizeof...(D0)>({D0...}),
+                              detail::first<sizeof...(D1)>({D1...}),
+                              detail::sum<sizeof...(D2)>({D2...}),
+                              detail::first<sizeof...(D3)>({D3...})>>,
+      detail::case_t<Dim == 3,
+                     Tensor4D<T, detail::first<sizeof...(D0)>({D0...}),
+                              detail::first<sizeof...(D1)>({D1...}),
+                              detail::first<sizeof...(D2)>({D2...}),
+                              detail::sum<sizeof...(D3)>({D3...})>>>::type;
+};
+namespace emitc {
+template <typename Src> inline Src abs(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::abs);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src ceil(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::ceil);
+  return unary<Src>(x, f);
+}
+template <typename Dest, typename Src> inline Dest convert(Src x) {
+  using ET_Dest = typename get_element_type<Dest>::type;
+  using ET_Src = typename get_element_type<Src>::type;
+  auto cast = [](ET_Src value) { return static_cast<ET_Dest>(value); };
+  return unary<Dest, Src, UnaryFuncType<ET_Dest, ET_Src>>(x, cast);
+}
+template <typename Src> inline Src exp(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::exp);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src floor(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::floor);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src log(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::log);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src negate(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = std::negate<ET_Src>{};
+  return unary<Src>(x, f);
+}
+template <typename Min, typename Src, typename Max>
+inline Src clamp(Min min, Src operand, Max max) {
+  static_assert(
+      std::is_same<Min, Src>::value ||
+          (is_tensor_of_dim<0, Min>::value &&
+           std::is_same<typename get_element_type<Src>::type,
+                        typename get_element_type<Min>::type>::value),
+      "Expected the same type for min and operand or a 0-dim tensor of the "
+      "same element type for min");
+  static_assert(
+      std::is_same<Max, Src>::value ||
+          (is_tensor_of_dim<0, Max>::value &&
+           std::is_same<typename get_element_type<Src>::type,
+                        typename get_element_type<Max>::type>::value),
+      "Expected the same type for min and operand or a 0-dim tensor of the "
+      "same element type for max");
+  const bool broadcast_min = !std::is_same<Min, Src>::value;
+  const bool broadcast_max = !std::is_same<Max, Src>::value;
+  Src result;
+  for (size_t index = 0; index < Src::size(); index++) {
+    const auto value_min = broadcast_min ? min[0] : min[index];
+    const auto value_max = broadcast_max ? max[0] : max[index];
+    auto value = operand[index];
+    value = value < value_min ? value_min : value;
+    value = value > value_max ? value_max : value;
+    result[index] = value;
+  }
+  return result;
+}
+template <typename Src> inline Src sqrt(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::sqrt);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src tanh(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = static_cast<ET_Src (*)(ET_Src)>(std::tanh);
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src add(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = std::plus<ET_Src>{};
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src max(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f =
+      static_cast<const ET_Src &(*)(const ET_Src &, const ET_Src &)>(std::max);
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src min(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f =
+      static_cast<const ET_Src &(*)(const ET_Src &, const ET_Src &)>(std::min);
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src mul(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = std::multiplies<ET_Src>{};
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src pow(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src a, ET_Src b) -> ET_Src {
+    if (std::is_integral<ET_Src>::value) {
+      const bool negative = b < 0;
+      if (b < 0) {
+        b = -b;
+      }
+      ET_Src result = 1;
+      for (ET_Src i = 0; i < b; i++) {
+        result *= a;
+      }
+      if (negative) {
+        result = 1 / result;
+      }
+      return result;
+    } else {
+      return std::pow(a, b);
+    }
+  };
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src sub(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = std::minus<ET_Src>{};
+  return binary<Src>(x, y, f);
+}
+template <typename Dest, typename Src>
+inline Dest
+broadcast_in_dim(Src operand,
+                 llvm::emitc::Tensor<int64_t, Src::rank()> broadcast_dimensions) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  std::vector<size_t> retainedDimensions(Dest::rank());
+  std::iota(retainedDimensions.begin(), retainedDimensions.end(), 0);
+  retainedDimensions.erase(
+      std::remove_if(retainedDimensions.begin(), retainedDimensions.end(),
+                     [&broadcast_dimensions](size_t i) {
+                       return std::find(broadcast_dimensions.begin(),
+                                        broadcast_dimensions.end(),
+                                        i) == broadcast_dimensions.end();
+                     }),
+      retainedDimensions.end());
+  assert(retainedDimensions.size() == Src::rank());
+  Dest result;
+  for (size_t i = 0; i < result.size(); i++) {
+    auto dest_index = result.unravel_index(i);
+    std::array<size_t, Src::rank()> src_index;
+    for (size_t j = 0; j < src_index.size(); j++) {
+      src_index[j] = dest_index[broadcast_dimensions(j)];
+    }
+    for (size_t i = 0; i < src_index.size(); ++i) {
+      if (Src::shape()[i] == 1) {
+        src_index[i] = 0;
+      }
+    }
+    result[i] = operand[operand.ravel_index(src_index)];
+  }
+  return result;
+}
+template <typename Dest, typename Lhs, typename Rhs>
+Dest dot(Lhs lhs, Rhs rhs) {
+  static_assert(is_tensor_of_dim<2, Lhs>::value, "Expected 2 dimensional lhs");
+  static_assert(is_tensor_of_dim<2, Rhs>::value, "Expected 2 dimensional rhs");
+  static_assert(Lhs::dim(1) == Rhs::dim(0),
+                "Expected contracting dimension to match");
+  Dest output;
+  for (size_t m = 0; m < lhs.dim(0); m++) {
+    for (size_t n = 0; n < lhs.dim(1); n++) {
+      for (size_t k = 0; k < rhs.dim(1); k++) {
+        output(m, k) += lhs(m, n) * rhs(n, k);
+      }
+    }
+  }
+  return output;
+}
+template <typename Dest, typename Lhs, typename Rhs>
+Dest batch_matmul(Lhs lhs, Rhs rhs) {
+  static_assert(is_tensor_of_dim<3, Lhs>::value, "Expected 3 dimensional lhs");
+  static_assert(is_tensor_of_dim<3, Rhs>::value, "Expected 3 dimensional rhs");
+  static_assert(Lhs::dim(0) == Rhs::dim(0) && Lhs::dim(0) == Dest::dim(0),
+                "Expected batch dimension to match");
+  static_assert(Lhs::dim(2) == Rhs::dim(1),
+                "Expected contracting dimension to match");
+  static_assert(Dest::dim(1) == Lhs::dim(1), "Expected row dimension to match");
+  static_assert(Dest::dim(2) == Rhs::dim(2),
+                "Expected column dimension to match");
+  Dest output;
+  for (size_t b = 0; b < lhs.dim(0); b++) {
+    for (size_t m = 0; m < lhs.dim(1); m++) {
+      for (size_t n = 0; n < lhs.dim(2); n++) {
+        for (size_t k = 0; k < rhs.dim(2); k++) {
+          output(b, m, k) += lhs(b, m, n) * rhs(b, n, k);
+        }
+      }
+    }
+  }
+  return output;
+}
+template <int64_t Dimension, typename Dest, typename Src>
+inline Dest concatenate(Src input) {
+  Dest z = input;
+  return z;
+}
+template <int64_t Dimension, typename Dest, typename Src1, typename... Src>
+inline Dest concatenate(Src1 input1, Src... inputs) {
+  static_assert(sizeof...(inputs) > 0, "Wrong template specialization chosen");
+  using ET_Src = typename get_element_type<Src1>::type;
+  using Rest = typename concat<Dimension, ET_Src, Src...>::type;
+  Rest rest = concatenate<Dimension, Rest, Src...>(inputs...);
+  Dest z;
+  auto calculate_shift = [](const auto &shape) {
+    size_t shift = 1;
+    for (size_t i = Dimension; i < shape.size(); i++) {
+      shift *= shape[i];
+    }
+    return shift;
+  };
+  auto a_shift = calculate_shift(Src1::shape());
+  auto b_shift = calculate_shift(Rest::shape());
+  for (auto a_ptr = input1.begin(), b_ptr = rest.begin(), c_ptr = z.begin();
+       a_ptr != input1.end(); a_ptr += a_shift, b_ptr += b_shift) {
+    std::copy(a_ptr, a_ptr + a_shift, c_ptr);
+    c_ptr += a_shift;
+    std::copy(b_ptr, b_ptr + b_shift, c_ptr);
+    c_ptr += b_shift;
+  }
+  return z;
+}
+template <typename Dest, typename Src> inline Dest reshape(Src x) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  using ET_Src = typename get_element_type<Src>::type;
+  using ET_Dest = typename get_element_type<Dest>::type;
+  static_assert(std::is_same<ET_Src, ET_Dest>::value, "Element type mismatch");
+  static_assert(Src::size() == Dest::size(), "Tensor size mismatch");
+  Dest z;
+  std::copy(x.begin(), x.end(), z.begin());
+  return z;
+}
+template <typename Dest, typename Src, IsTensorOfDim<1, Src> = true>
+Dest slice(Src x, llvm::emitc::Tensor<int64_t, 1> start_indices,
+           llvm::emitc::Tensor<int64_t, 1> limit_indices, llvm::emitc::Tensor<int64_t, 1> strides) {
+  Dest z;
+  size_t index = 0;
+  for (int64_t i = start_indices[0]; i < limit_indices[0]; i += strides[0]) {
+    z[index++] = x(i);
+  }
+  return z;
+}
+template <typename Dest, typename Src, IsTensorOfDim<2, Src> = true>
+Dest slice(Src x, llvm::emitc::Tensor<int64_t, 2> start_indices,
+           llvm::emitc::Tensor<int64_t, 2> limit_indices, llvm::emitc::Tensor<int64_t, 2> strides) {
+  Dest z;
+  size_t index = 0;
+  for (int64_t i = start_indices[0]; i < limit_indices[0]; i += strides[0]) {
+    for (int64_t j = start_indices[1]; j < limit_indices[1]; j += strides[1]) {
+      z[index++] = x(i, j);
+    }
+  }
+  return z;
+}
+template <typename Dest, typename Src, IsTensorOfDim<3, Src> = true>
+Dest slice(Src x, llvm::emitc::Tensor<int64_t, 3> start_indices,
+           llvm::emitc::Tensor<int64_t, 3> limit_indices, llvm::emitc::Tensor<int64_t, 3> strides) {
+  Dest z;
+  size_t index = 0;
+  for (int64_t i = start_indices[0]; i < limit_indices[0]; i += strides[0]) {
+    for (int64_t j = start_indices[1]; j < limit_indices[1]; j += strides[1]) {
+      for (int64_t k = start_indices[2]; k < limit_indices[2];
+           k += strides[2]) {
+        z[index++] = x(i, j, k);
+      }
+    }
+  }
+  return z;
+}
+template <typename Dest, typename Src, IsTensorOfDim<4, Src> = true>
+Dest slice(Src x, llvm::emitc::Tensor<int64_t, 4> start_indices,
+           llvm::emitc::Tensor<int64_t, 4> limit_indices, llvm::emitc::Tensor<int64_t, 4> strides) {
+  Dest z;
+  size_t index = 0;
+  for (int64_t i = start_indices[0]; i < limit_indices[0]; i += strides[0]) {
+    for (int64_t j = start_indices[1]; j < limit_indices[1]; j += strides[1]) {
+      for (int64_t k = start_indices[2]; k < limit_indices[2];
+           k += strides[2]) {
+        for (int64_t c = start_indices[3]; c < limit_indices[3];
+             c += strides[3]) {
+          z[index++] = x(i, j, k, c);
+        }
+      }
+    }
+  }
+  return z;
+}
+template <typename Dest, typename Src>
+inline Dest pad(Src operand,
+                llvm::emitc::Tensor<typename get_element_type<Src>::type> padding_value,
+                llvm::emitc::Tensor<int64_t, Src::rank()> edge_padding_low,
+                llvm::emitc::Tensor<int64_t, Src::rank()> edge_padding_high,
+                llvm::emitc::Tensor<int64_t, Src::rank()> interior_padding) {
+  assert(std::all_of(interior_padding.begin(), interior_padding.end(),
+                     [](int64_t i) { return i >= 0; }));
+  assert(std::all_of(edge_padding_low.begin(), edge_padding_low.end(),
+                     [](int64_t i) { return i >= 0; }));
+  assert(std::all_of(edge_padding_high.begin(), edge_padding_high.end(),
+                     [](int64_t i) { return i >= 0; }));
+  Dest result;
+  auto interior = [&interior_padding](std::array<size_t, Src::rank()> index) {
+    for (size_t i = 0; i < index.size(); i++) {
+      if (index[i] % (interior_padding[i] + 1) != 0) {
+        return true;
+      }
+    }
+    return false;
+  };
+  auto out_of_bounds = [](std::array<size_t, Src::rank()> index) {
+    for (size_t i = 0; i < index.size(); i++) {
+      if (index[i] < 0 || index[i] >= Src::dim(i)) {
+        return true;
+      }
+    }
+    return false;
+  };
+  for (size_t i = 0; i < result.size(); i++) {
+    auto index = result.unravel_index(i);
+    for (size_t j = 0; j < index.size(); j++) {
+      index[j] -= edge_padding_low[j];
+    }
+    if (interior(index)) {
+      result[i] = padding_value();
+    } else {
+      for (size_t j = 0; j < index.size(); j++) {
+        size_t pad = interior_padding[j];
+        assert(index[j] % (pad + 1) == 0);
+        index[j] /= (pad + 1);
+      }
+      if (out_of_bounds(index)) {
+        result[i] = padding_value();
+      } else {
+        result[i] = operand[operand.ravel_index(index)];
+      }
+    }
+  }
+  return result;
+}
+} // namespace emitc
+namespace emitc {
+namespace tensor {
+template <typename T, size_t... Shape, typename... Indices>
+inline T extract(llvm::emitc::Tensor<T, Shape...> x, Indices... indices) {
+  return x(indices...);
+}
+template <typename Dest, typename Src, IsScalar<Src> = true>
+inline Dest splat(Src x) {
+  Dest z;
+  std::fill(z.begin(), z.end(), x);
+  return z;
+}
+} // namespace tensor
+} // namespace emitc
+namespace emitc {
+namespace tosa {
+template <typename Src> inline Src abs(Src x) { return emitc::abs<Src>(x); }
+template <typename Dest, typename Src> inline Dest cast(Src x) {
+  return emitc::convert<Dest>(x);
+}
+template <typename Src> inline Src ceil(Src x) { return emitc::ceil<Src>(x); }
+template <typename Src>
+inline Src clamp(Src operand, typename Src::value_type min_value,
+                 typename Src::value_type max_value) {
+  llvm::emitc::Tensor<typename Src::value_type> min{min_value};
+  llvm::emitc::Tensor<typename Src::value_type> max{max_value};
+  return emitc::clamp(min, operand, max);
+}
+template <typename Src> inline Src clz(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  static_assert(std::is_same<ET_Src, int32_t>::value,
+                "Expected tensor of type int32_t");
+  auto f = [](ET_Src element) {
+    ET_Src count = 32;
+    while (element != 0 && count > 0) {
+      count--;
+      element >>= 1;
+    }
+    return count;
+  };
+  return unary<Src>(x, f);
+}
+template <typename Src> inline Src exp(Src x) { return emitc::exp<Src>(x); }
+template <typename Src> inline Src floor(Src x) { return emitc::floor<Src>(x); }
+template <typename Src> inline Src log(Src x) { return emitc::log<Src>(x); }
+template <typename Src> inline Src negate(Src x) { return emitc::negate(x); }
+template <typename Src> inline Src reciprocal(Src x) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src element) { return (static_cast<ET_Src>(1.0) / element); };
+  return unary<Src>(x, f);
+}
+template <typename Dest, size_t Dim, typename Src>
+inline Dest rescale(Src x, typename get_element_type<Src>::type in_zp,
+                    typename get_element_type<Dest>::type out_zp,
+                    Tensor1D<int32_t, Dim> mult, Tensor1D<int32_t, Dim> shift,
+                    bool scale32, bool double_round, bool per_channel) {
+  using ET_Dest = typename get_element_type<Dest>::type;
+  using Dest_I32 = typename replace_element_type<int32_t, Dest>::type;
+  assert(!(!scale32 && double_round) &&
+         "Invalid combination of `scale32` and `double_round` arguments.");
+  auto apply_scale = [=](int64_t element, int64_t mult, int64_t shift) {
+    int64_t round = 1 << (shift - 1);
+    if (double_round && shift > 31) {
+      if (element >= 0)
+        round += 1 << 30;
+      else
+        round -= 1 << 30;
+    }
+    int64_t result = (element * mult + round) >> shift;
+    return static_cast<int32_t>(result);
+  };
+  Dest_I32 result;
+  for (size_t i = 0; i < x.size(); ++i) {
+    size_t index = per_channel ? x.unravel_index(i)[x.rank() - 1] : 0;
+    int64_t element = x[i] - in_zp;
+    int32_t scaled_element = apply_scale(element, mult[index], shift[index]);
+    result[i] = scaled_element + out_zp;
+  }
+  Tensor0D<int32_t> min{
+      static_cast<int32_t>(std::numeric_limits<ET_Dest>::min())};
+  Tensor0D<int32_t> max{
+      static_cast<int32_t>(std::numeric_limits<ET_Dest>::max())};
+  return cast<Dest>(emitc::clamp(min, result, max));
+}
+template <typename Src> inline Src tanh(Src x) { return emitc::tanh<Src>(x); }
+template <typename Src> inline Src add(Src x, Src y) {
+  return emitc::add<Src>(x, y);
+}
+template <typename Src>
+inline Src arithmetic_right_shift(Src x, Src y, bool round) {
+  using ET_Src = typename get_element_type<Src>::type;
+  std::function<ET_Src(ET_Src, ET_Src)> f;
+  if (round) {
+    f = [](ET_Src left, ET_Src right) {
+      ET_Src result = left >> right;
+      if (right > 0 && ((left >> (right - 1)) & 1) != 0) {
+        result++;
+      }
+      return result;
+    };
+  } else {
+    f = [](ET_Src left, ET_Src right) { return left >> right; };
+  }
+  return binary<Src>(x, y, f);
+}
+template <typename Dest, typename Src> inline Dest equal(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src left, ET_Src right) { return left == right; };
+  return binary<Dest, Src>(x, y, f);
+}
+template <typename Dest, typename Src> inline Dest greater_equal(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src left, ET_Src right) { return left >= right; };
+  return binary<Dest, Src>(x, y, f);
+}
+template <typename Src> inline Src logical_left_shift(Src x, Src y) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f = [](ET_Src left, ET_Src right) { return left << right; };
+  return binary<Src>(x, y, f);
+}
+template <typename Src> inline Src mul(Src x, Src y) {
+  return emitc::mul(x, y);
+}
+template <typename Src> inline Src maximum(Src x, Src y) {
+  return emitc::max(x, y);
+}
+template <typename Src> inline Src minimum(Src x, Src y) {
+  return emitc::min(x, y);
+}
+template <typename Src, IsTensorOfType<Src, int32_t> = true>
+inline Src mul(Src x, Src y, const int32_t shift) {
+  if (shift > 0) {
+    auto f = [&shift](int32_t x, int32_t y) -> int32_t {
+      int64_t result;
+      int64_t round = 1L << (shift - 1);
+      result = x * y + round;
+      result = result >> shift;
+      return static_cast<int32_t>(result);
+    };
+    return binary<Src>(x, y, f);
+  } else {
+    return emitc::mul(x, y);
+  }
+}
+template <typename Src> inline Src pow(Src x, Src y) {
+  return emitc::pow(x, y);
+}
+template <typename Src> inline Src sub(Src x, Src y) {
+  return emitc::sub<Src>(x, y);
+}
+template <size_t... Shape>
+inline llvm::emitc::Tensor<int8_t, Shape...> table(llvm::emitc::Tensor<int8_t, Shape...> x,
+                                      Tensor1D<int8_t, 256> table) {
+  auto f = [&table](int8_t element) {
+    return table(static_cast<int16_t>(element) + 128);
+  };
+  return unary<llvm::emitc::Tensor<int8_t, Shape...>>(x, f);
+}
+template <size_t... Shape>
+inline llvm::emitc::Tensor<int32_t, Shape...> table(llvm::emitc::Tensor<int16_t, Shape...> x,
+                                       Tensor1D<int16_t, 513> table) {
+  auto f = [&table](int16_t element) {
+    int32_t integer = (element >> 7) + 0x100;
+    int32_t fractional = element & 0x7F;
+    int32_t result_integer = table(integer);
+    int32_t result_fractional =
+        (table(integer + 1) - table(integer)) * fractional;
+    return (result_integer << 7) + result_fractional;
+  };
+  return unary<llvm::emitc::Tensor<int32_t, Shape...>>(x, f);
+}
+template <typename Dest, typename SrcPred, typename SrcOperand>
+inline Dest select(SrcPred a, SrcOperand b, SrcOperand c) {
+  using ET_Src_Pred = typename get_element_type<SrcPred>::type;
+  static_assert(std::is_same<ET_Src_Pred, bool>::value,
+                "Pred tensor type must be bool");
+  using ET_Src_Operand = typename get_element_type<SrcOperand>::type;
+  auto f = [](ET_Src_Pred pred, ET_Src_Operand on_true,
+              ET_Src_Operand on_false) { return pred ? on_true : on_false; };
+  return ternary<Dest, SrcPred, SrcOperand, SrcOperand>(a, b, c, f);
+}
+template <int32_t Dimension, typename Dest, typename... Src>
+inline Dest concat(Src... inputs) {
+  return emitc::concatenate<Dimension, Dest, Src...>(inputs...);
+}
+template <typename Dest, typename Src, typename Weights>
+Dest conv2d(Src input, Weights weights, Tensor1D<int64_t, 4> padding,
+            Tensor1D<int64_t, 2> stride, Tensor1D<int64_t, 2> dilation) {
+  static_assert(is_tensor_of_dim<4, Src>::value,
+                "Expected 4 dimensional input");
+  static_assert(is_tensor_of_dim<4, Dest>::value,
+                "Expected 4 dimensional output");
+  static_assert(is_tensor_of_dim<4, Weights>::value,
+                "Expected 4 dimensional weights");
+  assert(stride[0] > 0);
+  assert(stride[1] > 0);
+  assert(dilation[0] == 1);
+  assert(dilation[1] == 1);
+  const int N = input.dim(0);
+  const int H_IN = input.dim(1);
+  const int W_IN = input.dim(2);
+  const int C_IN = input.dim(3);
+  Dest output;
+  const int C_OUT = output.dim(3);
+  const int K_H = weights.dim(1);
+  const int K_W = weights.dim(2);
+  const int S_H = stride[0];
+  const int S_W = stride[1];
+  const int pt = padding[0];
+  const int pb = padding[1];
+  const int pl = padding[2];
+  const int pr = padding[3];
+  const int H_PAD = pt + H_IN + pb;
+  const int W_PAD = pl + W_IN + pr;
+  for (int n = 0; n < N; n++) {
+    for (int h_pad = 0; h_pad < H_PAD - K_H + 1; h_pad += S_H) {
+      for (int w_pad = 0; w_pad < W_PAD - K_W + 1; w_pad += S_W) {
+        for (int kh = 0; kh < K_H; kh++) {
+          for (int kw = 0; kw < K_W; kw++) {
+            for (int c_in = 0; c_in < C_IN; c_in++) {
+              for (int c_out = 0; c_out < C_OUT; c_out++) {
+                const int h_out = h_pad / S_H;
+                const int w_out = w_pad / S_W;
+                const int h_in = h_pad - pt + kh;
+                const int w_in = w_pad - pl + kw;
+                if (h_in < 0 || h_in >= H_IN || w_in < 0 || w_in >= W_IN)
+                  continue;
+                output(n, h_out, w_out, c_out) +=
+                    input(n, h_in, w_in, c_in) * weights(c_out, kh, kw, c_in);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return output;
+}
+template <typename Dest, typename Src, typename Weights>
+Dest depthwise_conv2d(Src input, Weights weights, Tensor1D<int64_t, 4> padding,
+                      Tensor1D<int64_t, 2> stride,
+                      Tensor1D<int64_t, 2> dilation) {
+  static_assert(is_tensor_of_dim<4, Src>::value,
+                "Expected 4 dimensional input");
+  static_assert(is_tensor_of_dim<4, Dest>::value,
+                "Expected 4 dimensional output");
+  static_assert(is_tensor_of_dim<4, Weights>::value,
+                "Expected 4 dimensional weights");
+  static_assert(Src::dim(3) == Weights::dim(2),
+                "Input channels must equal weights channels");
+  static_assert(Src::dim(0) == Dest::dim(0), "Batch sizes must be equal");
+  static_assert(Dest::dim(3) % Src::dim(3) == 0,
+                "Output channels need to be a multiple of input channels");
+  static_assert(
+      Dest::dim(3) == Src::dim(3) * Weights::dim(3),
+      "Output channels size must be input channels times channel multiplier");
+  assert(stride[0] > 0);
+  assert(stride[1] > 0);
+  assert(dilation[0] == 1);
+  assert(dilation[1] == 1);
+  const int N = input.dim(0);
+  const int H_IN = input.dim(1);
+  const int W_IN = input.dim(2);
+  const int C_IN = input.dim(3);
+  Dest output;
+  const int K_H = weights.dim(0);
+  const int K_W = weights.dim(1);
+  const int M = weights.dim(3);
+  const int S_H = stride[0];
+  const int S_W = stride[1];
+  const int pt = padding[0];
+  const int pb = padding[1];
+  const int pl = padding[2];
+  const int pr = padding[3];
+  const int H_PAD = pt + H_IN + pb;
+  const int W_PAD = pl + W_IN + pr;
+  for (int n = 0; n < N; ++n) {
+    for (int h_pad = 0; h_pad < H_PAD - K_H + 1; h_pad += S_H) {
+      for (int w_pad = 0; w_pad < W_PAD - K_W + 1; w_pad += S_W) {
+        for (int kh = 0; kh < K_H; ++kh) {
+          for (int kw = 0; kw < K_W; ++kw) {
+            for (int c_in = 0; c_in < C_IN; ++c_in) {
+              for (int m = 0; m < M; ++m) {
+                const int h_out = h_pad / S_H;
+                const int w_out = w_pad / S_W;
+                const int c_out = c_in * M + m;
+                const int h_in = h_pad - pt + kh;
+                const int w_in = w_pad - pl + kw;
+                if (h_in < 0 || h_in >= H_IN || w_in < 0 || w_in >= W_IN)
+                  continue;
+                const size_t weights_index = emitc::utility::ravel_index<
+                    Weights::dim(0), Weights::dim(1), 1,
+                    Weights::dim(2) * Weights::dim(3)>(kh, kw, 0, c_out);
+                output(n, h_out, w_out, c_out) +=
+                    input(n, h_in, w_in, c_in) * weights[weights_index];
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return output;
+}
+template <typename Dest, typename Src, typename Weights, typename Bias>
+Dest fully_connected(Src input, Weights weights, Bias bias) {
+  static_assert(is_tensor_of_dim<2, Src>::value,
+                "Expected 2 dimensional input");
+  static_assert(is_tensor_of_dim<2, Dest>::value,
+                "Expected 2 dimensional output");
+  static_assert(is_tensor_of_dim<2, Weights>::value,
+                "Expected 2 dimensional weights");
+  static_assert(is_tensor_of_dim<1, Bias>::value,
+                "Expected 1 dimensional bias");
+  Dest output;
+  static_assert(input.dim(0) == output.dim(0),
+                "Output and input batch dimension do not match.");
+  static_assert(input.dim(1) == weights.dim(1),
+                "Input and weights dimensions do not match.");
+  static_assert(output.dim(1) == weights.dim(0),
+                "Output and weights dimensions do not match.");
+  static_assert(weights.dim(0) == bias.dim(0),
+                "Bias and weights dimensions do not match.");
+  const size_t N = input.dim(0);
+  const size_t C_IN = input.dim(1);
+  const size_t C_OUT = weights.dim(0);
+  for (size_t n = 0; n < N; ++n) {
+    for (size_t c_out = 0; c_out < C_OUT; ++c_out) {
+      for (size_t c_in = 0; c_in < C_IN; ++c_in) {
+        auto in = input(n, c_in);
+        auto weight = weights(c_out, c_in);
+        output(n, c_out) += in * weight;
+      }
+      output(n, c_out) += bias(c_out);
+    }
+  }
+  return output;
+}
+template <typename Dest, typename Src, typename Idx,
+          IsTensorOfDim<3, Dest> = true, IsTensorOfDim<3, Src> = true,
+          IsTensorOfDim<2, Idx> = true, IsTensorOfType<Idx, int32_t> = true>
+Dest gather(Src input, Idx indices) {
+  Dest result;
+  static_assert(input.dim(0) == result.dim(0),
+                "Input and output batch dimension do not match.");
+  static_assert(input.dim(0) == indices.dim(0),
+                "Input and weight batch dimension do not match.");
+  static_assert(input.dim(2) == result.dim(2),
+                "Input and output channel dimension do not match.");
+  static_assert(indices.dim(1) == result.dim(1),
+                "Weight and output index dimension do not match.");
+  auto it = result.begin();
+  size_t d0offset = Src::dim(1) * Src::dim(2);
+  for (size_t i = 0, idx = Idx::size(); i < idx; i++) {
+    auto d0 = d0offset * (i / Idx::dim(1));
+    auto d1 = Src::dim(2) * indices[i];
+    auto start = input.begin() + d0 + d1;
+    auto end = start + Src::dim(2);
+    it = std::copy(start, end, it);
+  }
+  return result;
+}
+template <typename T, size_t B, size_t M, size_t K, size_t N>
+Tensor3D<T, B, M, N> matmul(Tensor3D<T, B, M, K> a, Tensor3D<T, B, K, N> b) {
+  return emitc::batch_matmul<Tensor3D<T, B, M, N>>(a, b);
+}
+namespace {
+template <typename Dest, typename Src, typename Computation>
+inline Dest reduce(Src operand, typename get_element_type<Src>::type initValue,
+                   int64_t dimension, Computation computation) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  using ET_Src = typename get_element_type<Src>::type;
+  using ET_Dest = typename get_element_type<Dest>::type;
+  static_assert(std::is_same<ET_Src, ET_Dest>::value, "Element type mismatch");
+  static_assert(Src::rank() == Dest::rank() + 1,
+                "source rank must equal dest rank + 1");
+  std::vector<size_t> retainedDimensions(Src::rank());
+  std::iota(retainedDimensions.begin(), retainedDimensions.end(), 0);
+  retainedDimensions.erase(retainedDimensions.begin() + dimension);
+  assert(retainedDimensions.size() == Dest::rank());
+  Dest result;
+  std::fill(result.begin(), result.end(), initValue);
+  for (size_t i = 0; i < operand.size(); ++i) {
+    auto value = operand[i];
+    auto index = operand.unravel_index(i);
+    std::array<size_t, Dest::rank()> reducedIndex;
+    size_t j = 0;
+    for (size_t dim : retainedDimensions) {
+      reducedIndex[j++] = index[dim];
+    }
+    auto reductionValue = result[result.ravel_index(reducedIndex)];
+    result[result.ravel_index(reducedIndex)] =
+        computation(reductionValue, value);
+  }
+  return result;
+}
+} // namespace
+template <typename Dest, typename Src>
+inline Dest argmax(Src operand, int64_t dimension) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  using ET_Src = typename get_element_type<Src>::type;
+  static_assert(Src::rank() == Dest::rank() + 1,
+                "source rank must equal dest rank + 1");
+  std::vector<size_t> retainedDimensions(Src::rank());
+  std::iota(retainedDimensions.begin(), retainedDimensions.end(), 0);
+  retainedDimensions.erase(retainedDimensions.begin() + dimension);
+  assert(retainedDimensions.size() == Dest::rank());
+  Dest result;
+  typename replace_element_type<ET_Src, Dest>::type maxValues;
+  std::fill(maxValues.begin(), maxValues.end(),
+            std::numeric_limits<ET_Src>::min());
+  for (size_t i = 0; i < operand.size(); ++i) {
+    auto value = operand[i];
+    auto index = operand.unravel_index(i);
+    std::array<size_t, Dest::rank()> reducedIndex;
+    size_t j = 0;
+    for (size_t dim : retainedDimensions) {
+      reducedIndex[j++] = index[dim];
+    }
+    auto destIndex = result.ravel_index(reducedIndex);
+    if (value > maxValues[destIndex]) {
+      maxValues[destIndex] = value;
+      result[destIndex] = index[dimension];
+    }
+  }
+  return result;
+}
+template <typename Dest, typename Src>
+inline Dest reduce_all(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  using ET_Dest = typename get_element_type<Dest>::type;
+  static_assert(std::is_same<ET_Src, bool>::value,
+                "Src tensor type must be bool");
+  static_assert(std::is_same<ET_Dest, bool>::value,
+                "Dest tensor type must be bool");
+  auto and_ = [](ET_Src a, ET_Src b) { return (a && b); };
+  return tosa::reduce<Dest, Src>(input, true, dimension, and_);
+}
+template <typename Dest, typename Src>
+inline Dest reduce_any(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  using ET_Dest = typename get_element_type<Dest>::type;
+  static_assert(std::is_same<ET_Src, bool>::value,
+                "Src tensor type must be bool");
+  static_assert(std::is_same<ET_Dest, bool>::value,
+                "Dest tensor type must be bool");
+  auto or_ = [](ET_Src a, ET_Src b) { return a || b; };
+  return tosa::reduce<Dest, Src>(input, false, dimension, or_);
+}
+template <typename Dest, typename Src>
+inline Dest reduce_max(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f =
+      static_cast<const ET_Src &(*)(const ET_Src &, const ET_Src &)>(std::max);
+  return tosa::reduce<Dest, Src>(input, std::numeric_limits<ET_Src>::min(),
+                                 dimension, f);
+}
+template <typename Dest, typename Src>
+inline Dest reduce_min(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  auto f =
+      static_cast<const ET_Src &(*)(const ET_Src &, const ET_Src &)>(std::min);
+  return tosa::reduce<Dest, Src>(input, std::numeric_limits<ET_Src>::max(),
+                                 dimension, f);
+}
+template <typename Dest, typename Src>
+inline Dest reduce_prod(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  return tosa::reduce<Dest, Src>(input, 1, dimension,
+                                 std::multiplies<ET_Src>{});
+}
+template <typename Dest, typename Src>
+inline Dest reduce_sum(Src input, int64_t dimension) {
+  using ET_Src = typename get_element_type<Src>::type;
+  return tosa::reduce<Dest, Src>(input, 0, dimension, std::plus<ET_Src>{});
+}
+template <typename Dest, typename Src> inline Dest reshape(Src x) {
+  return emitc::reshape<Dest>(x);
+}
+template <typename Dest, typename Src>
+Dest slice(Src x, llvm::emitc::Tensor<int64_t, Src::rank()> start_indices,
+           llvm::emitc::Tensor<int64_t, Src::rank()> slice_sizes) {
+  llvm::emitc::Tensor<int64_t, Src::rank()> limit_indices =
+      emitc::add(start_indices, slice_sizes);
+  llvm::emitc::Tensor<int64_t, Src::rank()> strides =
+      emitc::tensor::splat<llvm::emitc::Tensor<int64_t, Src::rank()>>(1);
+  return emitc::slice<Dest, Src>(x, start_indices, limit_indices, strides);
+}
+template <typename Dest, typename Src, typename Padding>
+inline Dest pad(Src operand, Padding padding,
+                Tensor0D<typename get_element_type<Src>::type> pad_const =
+                    Tensor0D<typename get_element_type<Src>::type>{0}) {
+  using ET_Padding = typename get_element_type<Padding>::type;
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Padding>::value, "Expected tensor argument");
+  static_assert(Padding::rank() == 2, "Padding must have rank 2");
+  static_assert(Padding::dim(0) == Src::rank(),
+                "Dimension 1 of padding must equal source rank");
+  static_assert(Padding::dim(1) == 2, "Dimension 2 of padding is must be 2");
+  static_assert(std::is_same<ET_Padding, int32_t>::value ||
+                    std::is_same<ET_Padding, int64_t>::value,
+                "Padding element type must be i32 or i64");
+  llvm::emitc::Tensor<int64_t, Src::rank()> edge_padding_low;
+  llvm::emitc::Tensor<int64_t, Src::rank()> edge_padding_high;
+  for (unsigned int i = 0; i < padding.dim(0); ++i) {
+    edge_padding_low(i) = padding(i, 0);
+    edge_padding_high(i) = padding(i, 1);
+  }
+  llvm::emitc::Tensor<int64_t, Src::rank()> interior_padding;
+  std::fill(interior_padding.begin(), interior_padding.end(), 0);
+  return emitc::pad<Dest>(operand, pad_const, edge_padding_low,
+                          edge_padding_high, interior_padding);
+}
+template <typename Dest, typename Src, IsTensorOfDim<1, Dest> = true>
+Dest tile(Src input, Tensor1D<int64_t, 1> multiples) {
+  Dest result;
+  auto it = result.begin();
+  for (int32_t i = 0, M0 = multiples[0]; i < M0; i++) {
+    it = std::copy(input.begin(), input.end(), it);
+  }
+  return result;
+}
+template <typename Dest, typename Src, IsTensorOfDim<2, Src> = true>
+Dest tile(Src input, Tensor1D<int64_t, 2> multiples) {
+  Dest result;
+  auto it = result.begin();
+  for (int32_t i = 0, M0 = multiples[0]; i < M0; i++) {
+    for (int32_t j = 0, D0 = Src::dim(0); j < D0; j++) {
+      for (int32_t k = 0, M1 = multiples[1]; k < M1; k++) {
+        auto start = input.begin() + j * Src::dim(1);
+        auto end = start + Src::dim(1);
+        it = std::copy(start, end, it);
+      }
+    }
+  }
+  return result;
+}
+template <typename Dest, typename Src, IsTensorOfDim<3, Src> = true>
+Dest tile(Src input, Tensor1D<int64_t, 3> multiples) {
+  Dest result;
+  auto it = result.begin();
+  for (int32_t m0 = 0, M0 = multiples[0]; m0 < M0; m0++) {
+    for (int32_t d0 = 0, D0 = Src::dim(0); d0 < D0; d0++) {
+      for (int32_t m1 = 0, M1 = multiples[1]; m1 < M1; m1++) {
+        for (int32_t d1 = 0, D1 = Src::dim(1); d1 < D1; d1++) {
+          for (int32_t m2 = 0, M2 = multiples[2]; m2 < M2; m2++) {
+            auto start = input.begin() + (d0 * Src::dim(1) + d1) * Src::dim(2);
+            auto end = start + Src::dim(2);
+            it = std::copy(start, end, it);
+          }
+        }
+      }
+    }
+  }
+  return result;
+}
+template <typename Dest, typename Src, IsTensorOfDim<4, Src> = true>
+Dest tile(Src input, Tensor1D<int64_t, 4> multiples) {
+  Dest result;
+  auto it = result.begin();
+  for (int32_t m0 = 0, M0 = multiples[0]; m0 < M0; m0++) {
+    for (int32_t d0 = 0, D0 = Src::dim(0); d0 < D0; d0++) {
+      for (int32_t m1 = 0, M1 = multiples[1]; m1 < M1; m1++) {
+        for (int32_t d1 = 0, D1 = Src::dim(1); d1 < D1; d1++) {
+          for (int32_t m2 = 0, M2 = multiples[2]; m2 < M2; m2++) {
+            for (int32_t d2 = 0, D2 = Src::dim(2); d2 < D2; d2++) {
+              for (int32_t m3 = 0, M3 = multiples[3]; m3 < M3; m3++) {
+                auto start =
+                    input.begin() +
+                    ((d0 * Src::dim(1) + d1) * Src::dim(2) + d2) * Src::dim(3);
+                auto end = start + Src::dim(3);
+                it = std::copy(start, end, it);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return result;
+}
+template <typename Dest, typename Src>
+inline Dest transpose(Src operand, Tensor1D<int64_t, Src::rank()> perms) {
+  static_assert(is_tensor<Src>::value, "Expected tensor argument");
+  static_assert(is_tensor<Dest>::value, "Expected tensor result");
+  Tensor1D<int64_t, Src::rank()> broadcast_dimensions;
+  for (size_t i = 0; i < perms.size(); ++i) {
+    auto pos = std::find(perms.begin(), perms.end(), i);
+    assert(pos != std::end(perms));
+    int64_t index = std::distance(perms.begin(), pos);
+    broadcast_dimensions[i] = index;
+  }
+  return emitc::broadcast_in_dim<Dest>(operand, broadcast_dimensions);
+}
+template <typename Dest, typename Src>
+inline Dest transpose(Src input, Tensor1D<int32_t, Src::rank()> perms) {
+  Tensor1D<int64_t, Src::rank()> permsInt64;
+  for (size_t i = 0; i < perms.size(); ++i) {
+    permsInt64[i] = static_cast<int64_t>(perms[i]);
+  }
+  return tosa::transpose<Dest>(input, permsInt64);
+}
+} // namespace tosa
+} // namespace emitc
+} // namespace
+class action final : public llvm::MLInlineOzEmitCModel {
+private:
+  llvm::emitc::Tensor<int64_t, 1> v1;
+  llvm::emitc::Tensor<int64_t, 1> v2;
+  llvm::emitc::Tensor<int64_t, 1> v3;
+  llvm::emitc::Tensor<int64_t, 1> v4;
+  llvm::emitc::Tensor<int64_t, 1> v5;
+  llvm::emitc::Tensor<int64_t, 1> v6;
+  llvm::emitc::Tensor<int64_t, 1> v7;
+  llvm::emitc::Tensor<int64_t, 1> v8;
+  llvm::emitc::Tensor<int64_t, 1> v9;
+  llvm::emitc::Tensor<int64_t, 1> v10;
+  llvm::emitc::Tensor<int64_t, 1> v11;
+  llvm::emitc::Tensor<int64_t, 1> v12;
+  llvm::emitc::Tensor<int64_t, 1> v13;
+  llvm::emitc::Tensor<int64_t, 1> v14;
+  llvm::emitc::Tensor<int64_t, 1> v15;
+  llvm::emitc::Tensor<int64_t, 1> v16;
+  llvm::emitc::Tensor<int64_t, 1> v17;
+  llvm::emitc::Tensor<int64_t, 1> v18;
+  llvm::emitc::Tensor<int64_t, 1> v19;
+  llvm::emitc::Tensor<int64_t, 1> v20;
+  llvm::emitc::Tensor<int64_t, 1> v21;
+  llvm::emitc::Tensor<int64_t, 1> v22;
+  llvm::emitc::Tensor<int64_t, 1> v23;
+  llvm::emitc::Tensor<int64_t, 1> v24;
+  llvm::emitc::Tensor<int64_t, 1> v25;
+  llvm::emitc::Tensor<int64_t, 1> v26;
+  llvm::emitc::Tensor<int64_t, 1> v27;
+  llvm::emitc::Tensor<int32_t, 1> v28;
+  llvm::emitc::Tensor<int64_t, 1> v29;
+  llvm::emitc::Tensor<int64_t, 1> v30;
+  llvm::emitc::Tensor<int64_t, 1> v31;
+  llvm::emitc::Tensor<int64_t, 1> v32;
+  llvm::emitc::Tensor<int64_t, 1> v33;
+  llvm::emitc::Tensor<int64_t, 1> v34;
+  llvm::emitc::Tensor<int64_t, 1> v35;
+  llvm::emitc::Tensor<int64_t, 1> v36;
+  llvm::emitc::Tensor<float, 1> v37;
+  llvm::emitc::Tensor<int64_t, 1> v38;
+  llvm::emitc::Tensor<float, 1> v39;
+
+public:
+  virtual ~action() {}
+
+  void set_callsite_cost(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_is_multiple_blocks(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_caller_conditionally_executed_blocks(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_inlining_default(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_cold_cc_penalty(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_callee_conditionally_executed_blocks(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_callee_users(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_callee_basic_block_count(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_nr_ctant_params(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_load_relative_intrinsic(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_jump_table_penalty(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_unsimplified_common_instructions(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_indirect_call_penalty(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_load_elimination(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_call_penalty(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_cost_estimate(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_case_cluster_penalty(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_node_count(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_call_argument_setup(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_sroa_savings(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_lowered_call_arg_setup(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_threshold(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_dead_blocks(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_constant_args(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_sroa_losses(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_simplified_instructions(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_num_loops(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_step_type(llvm::emitc::Tensor<int32_t, 1> x) override;
+  void set_edge_count(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_nested_inlines(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_caller_basic_block_count(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_last_call_to_static_bonus(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_nested_inline_cost_estimate(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_callsite_height(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_constant_offset_ptr_args(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_switch_penalty(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_discount(llvm::emitc::Tensor<float, 1> x) override;
+  void set_caller_users(llvm::emitc::Tensor<int64_t, 1> x) override;
+  void set_reward(llvm::emitc::Tensor<float, 1> x) override;
+  std::string name() override { return "mymodel"; }
+  llvm::emitc::Tensor<int64_t, 1> run() override;
+};
+void action::set_callsite_cost(llvm::emitc::Tensor<int64_t, 1> x) { v1 = std::move(x); }
+void action::set_is_multiple_blocks(llvm::emitc::Tensor<int64_t, 1> x) { v2 = std::move(x); }
+void action::set_caller_conditionally_executed_blocks(llvm::emitc::Tensor<int64_t, 1> x) {
+  v3 = std::move(x);
+}
+void action::set_inlining_default(llvm::emitc::Tensor<int64_t, 1> x) { v4 = std::move(x); }
+void action::set_cold_cc_penalty(llvm::emitc::Tensor<int64_t, 1> x) { v5 = std::move(x); }
+void action::set_callee_conditionally_executed_blocks(llvm::emitc::Tensor<int64_t, 1> x) {
+  v6 = std::move(x);
+}
+void action::set_callee_users(llvm::emitc::Tensor<int64_t, 1> x) { v7 = std::move(x); }
+void action::set_callee_basic_block_count(llvm::emitc::Tensor<int64_t, 1> x) {
+  v8 = std::move(x);
+}
+void action::set_nr_ctant_params(llvm::emitc::Tensor<int64_t, 1> x) { v9 = std::move(x); }
+void action::set_load_relative_intrinsic(llvm::emitc::Tensor<int64_t, 1> x) {
+  v10 = std::move(x);
+}
+void action::set_jump_table_penalty(llvm::emitc::Tensor<int64_t, 1> x) {
+  v11 = std::move(x);
+}
+void action::set_unsimplified_common_instructions(llvm::emitc::Tensor<int64_t, 1> x) {
+  v12 = std::move(x);
+}
+void action::set_indirect_call_penalty(llvm::emitc::Tensor<int64_t, 1> x) {
+  v13 = std::move(x);
+}
+void action::set_load_elimination(llvm::emitc::Tensor<int64_t, 1> x) { v14 = std::move(x); }
+void action::set_call_penalty(llvm::emitc::Tensor<int64_t, 1> x) { v15 = std::move(x); }
+void action::set_cost_estimate(llvm::emitc::Tensor<int64_t, 1> x) { v16 = std::move(x); }
+void action::set_case_cluster_penalty(llvm::emitc::Tensor<int64_t, 1> x) {
+  v17 = std::move(x);
+}
+void action::set_node_count(llvm::emitc::Tensor<int64_t, 1> x) { v18 = std::move(x); }
+void action::set_call_argument_setup(llvm::emitc::Tensor<int64_t, 1> x) {
+  v19 = std::move(x);
+}
+void action::set_sroa_savings(llvm::emitc::Tensor<int64_t, 1> x) { v20 = std::move(x); }
+void action::set_lowered_call_arg_setup(llvm::emitc::Tensor<int64_t, 1> x) {
+  v21 = std::move(x);
+}
+void action::set_threshold(llvm::emitc::Tensor<int64_t, 1> x) { v22 = std::move(x); }
+void action::set_dead_blocks(llvm::emitc::Tensor<int64_t, 1> x) { v23 = std::move(x); }
+void action::set_constant_args(llvm::emitc::Tensor<int64_t, 1> x) { v24 = std::move(x); }
+void action::set_sroa_losses(llvm::emitc::Tensor<int64_t, 1> x) { v25 = std::move(x); }
+void action::set_simplified_instructions(llvm::emitc::Tensor<int64_t, 1> x) {
+  v26 = std::move(x);
+}
+void action::set_num_loops(llvm::emitc::Tensor<int64_t, 1> x) { v27 = std::move(x); }
+void action::set_step_type(llvm::emitc::Tensor<int32_t, 1> x) { v28 = std::move(x); }
+void action::set_edge_count(llvm::emitc::Tensor<int64_t, 1> x) { v29 = std::move(x); }
+void action::set_nested_inlines(llvm::emitc::Tensor<int64_t, 1> x) { v30 = std::move(x); }
+void action::set_caller_basic_block_count(llvm::emitc::Tensor<int64_t, 1> x) {
+  v31 = std::move(x);
+}
+void action::set_last_call_to_static_bonus(llvm::emitc::Tensor<int64_t, 1> x) {
+  v32 = std::move(x);
+}
+void action::set_nested_inline_cost_estimate(llvm::emitc::Tensor<int64_t, 1> x) {
+  v33 = std::move(x);
+}
+void action::set_callsite_height(llvm::emitc::Tensor<int64_t, 1> x) { v34 = std::move(x); }
+void action::set_constant_offset_ptr_args(llvm::emitc::Tensor<int64_t, 1> x) {
+  v35 = std::move(x);
+}
+void action::set_switch_penalty(llvm::emitc::Tensor<int64_t, 1> x) { v36 = std::move(x); }
+void action::set_discount(llvm::emitc::Tensor<float, 1> x) { v37 = std::move(x); }
+void action::set_caller_users(llvm::emitc::Tensor<int64_t, 1> x) { v38 = std::move(x); }
+void action::set_reward(llvm::emitc::Tensor<float, 1> x) { v39 = std::move(x); }
+llvm::emitc::Tensor<int64_t, 1> action::run() {
+  llvm::emitc::Tensor<float, 1, 1> v40 = {(float)1.001000990e-03};
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v41 = {
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  llvm::emitc::Tensor<float, 1, 1> v42 = {(float)1.000100020e-04};
+  llvm::emitc::Tensor<int64_t, 1> v43 = {1};
+  llvm::emitc::Tensor<int64_t, 1> v44 = {0};
+  llvm::emitc::Tensor<float, 999> v45 = {
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+      (float)1.110000000e+02, (float)1.130000000e+02, (float)1.170000000e+02,
+      (float)1.190000000e+02, (float)1.210000000e+02, (float)1.240000000e+02,
+      (float)1.270000000e+02, (float)1.290000000e+02, (float)1.320000000e+02,
+      (float)1.350000000e+02, (float)1.370000000e+02, (float)1.420000000e+02,
+      (float)1.450000000e+02, (float)1.470000000e+02, (float)1.510000000e+02,
+      (float)1.540000000e+02, (float)1.570000000e+02, (float)1.600000000e+02,
+      (float)1.650000000e+02, (float)1.670000000e+02, (float)1.720000000e+02,
+      (float)1.770000000e+02, (float)1.820000000e+02, (float)1.890000000e+02,
+      (float)1.940000000e+02, (float)1.990000000e+02, (float)2.050000000e+02,
+      (float)2.110000000e+02, (float)2.170000000e+02, (float)2.280000000e+02,
+      (float)2.360000000e+02, (float)2.470000000e+02, (float)2.540000000e+02,
+      (float)2.610000000e+02, (float)2.690000000e+02, (float)2.750000000e+02,
+      (float)2.850000000e+02, (float)2.900000000e+02, (float)2.980000000e+02,
+      (float)3.060000000e+02, (float)3.170000000e+02, (float)3.290000000e+02,
+      (float)3.370000000e+02, (float)3.520000000e+02, (float)3.630000000e+02,
+      (float)3.730000000e+02, (float)3.950000000e+02, (float)4.050000000e+02,
+      (float)4.170000000e+02, (float)4.370000000e+02, (float)4.520000000e+02,
+      (float)4.730000000e+02, (float)4.960000000e+02, (float)5.210000000e+02,
+      (float)5.330000000e+02, (float)5.510000000e+02, (float)5.660000000e+02,
+      (float)5.850000000e+02, (float)6.070000000e+02, (float)6.240000000e+02,
+      (float)6.580000000e+02, (float)6.830000000e+02, (float)7.240000000e+02,
+      (float)7.610000000e+02, (float)7.780000000e+02, (float)8.130000000e+02,
+      (float)8.960000000e+02, (float)9.400000000e+02, (float)9.930000000e+02,
+      (float)1.047000000e+03, (float)1.137000000e+03, (float)1.269000000e+03,
+      (float)1.379000000e+03, (float)1.669000000e+03, (float)1.810000000e+03,
+      (float)2.076000000e+03, (float)2.230000000e+03, (float)3.920000000e+03};
+  llvm::emitc::Tensor<float, 999> v48 = {
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+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)5.000000000e+00, (float)6.000000000e+00, (float)6.000000000e+00,
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+      (float)6.000000000e+00, (float)6.000000000e+00, (float)6.000000000e+00,
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+      (float)8.000000000e+00, (float)8.000000000e+00, (float)9.000000000e+00,
+      (float)9.000000000e+00, (float)9.000000000e+00, (float)9.000000000e+00,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.100000000e+01,
+      (float)1.100000000e+01, (float)1.200000000e+01, (float)1.200000000e+01,
+      (float)1.300000000e+01, (float)1.400000000e+01, (float)1.500000000e+01,
+      (float)1.700000000e+01, (float)1.900000000e+01, (float)2.100000000e+01,
+      (float)2.600000000e+01, (float)3.300000000e+01, (float)4.900000000e+01};
+  llvm::emitc::Tensor<float, 999> v50 = {
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+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
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+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)5.000000000e+00,  (float)5.000000000e+00,
+      (float)5.000000000e+00,  (float)9.000000000e+00,  (float)9.000000000e+00,
+      (float)9.000000000e+00,  (float)9.000000000e+00,  (float)1.200000000e+01};
+  llvm::emitc::Tensor<float, 999> v53 = {
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+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.500000000e+01,
+      (float)1.500000000e+01, (float)1.500000000e+01, (float)1.500000000e+01,
+      (float)1.500000000e+01, (float)1.500000000e+01, (float)1.500000000e+01,
+      (float)1.500000000e+01, (float)1.500000000e+01, (float)1.500000000e+01,
+      (float)1.500000000e+01, (float)1.500000000e+01, (float)1.500000000e+01,
+      (float)1.500000000e+01, (float)1.500000000e+01, (float)1.500000000e+01,
+      (float)1.500000000e+01, (float)1.500000000e+01, (float)1.500000000e+01,
+      (float)1.500000000e+01, (float)1.500000000e+01, (float)1.500000000e+01,
+      (float)2.000000000e+01, (float)2.000000000e+01, (float)2.000000000e+01,
+      (float)2.000000000e+01, (float)2.000000000e+01, (float)2.500000000e+01,
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+      (float)3.500000000e+01, (float)4.500000000e+01, (float)5.000000000e+01};
+  llvm::emitc::Tensor<float, 999> v54 = {
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
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+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.000000000e+01, (float)1.500000000e+01, (float)2.500000000e+01};
+  llvm::emitc::Tensor<float, 999> v55 = {
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+  llvm::emitc::Tensor<float, 999> v56 = {
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)5.000000000e+01, (float)7.500000000e+01};
+  llvm::emitc::Tensor<float, 999> v57 = {
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)4.000000000e+00};
+  llvm::emitc::Tensor<float, 999> v58 = {
+      (float)3.600000000e+01, (float)5.600000000e+01, (float)7.100000000e+01,
+      (float)8.200000000e+01, (float)9.400000000e+01, (float)1.060000000e+02,
+      (float)1.140000000e+02, (float)1.220000000e+02, (float)1.310000000e+02,
+      (float)1.330000000e+02, (float)1.370000000e+02, (float)1.430000000e+02,
+      (float)1.510000000e+02, (float)1.570000000e+02, (float)1.630000000e+02,
+      (float)1.670000000e+02, (float)1.690000000e+02, (float)1.730000000e+02,
+      (float)1.760000000e+02, (float)1.800000000e+02, (float)1.830000000e+02,
+      (float)1.860000000e+02, (float)1.920000000e+02, (float)1.960000000e+02,
+      (float)2.000000000e+02, (float)2.030000000e+02, (float)2.050000000e+02,
+      (float)2.070000000e+02, (float)2.090000000e+02, (float)2.120000000e+02,
+      (float)2.170000000e+02, (float)2.210000000e+02, (float)2.230000000e+02,
+      (float)2.260000000e+02, (float)2.280000000e+02, (float)2.310000000e+02,
+      (float)2.320000000e+02, (float)2.350000000e+02, (float)2.390000000e+02,
+      (float)2.410000000e+02, (float)2.450000000e+02, (float)2.480000000e+02,
+      (float)2.520000000e+02, (float)2.550000000e+02, (float)2.580000000e+02,
+      (float)2.620000000e+02, (float)2.640000000e+02, (float)2.660000000e+02,
+      (float)2.690000000e+02, (float)2.720000000e+02, (float)2.750000000e+02,
+      (float)2.780000000e+02, (float)2.820000000e+02, (float)2.850000000e+02,
+      (float)2.880000000e+02, (float)2.900000000e+02, (float)2.930000000e+02,
+      (float)2.960000000e+02, (float)2.980000000e+02, (float)3.010000000e+02,
+      (float)3.030000000e+02, (float)3.050000000e+02, (float)3.080000000e+02,
+      (float)3.100000000e+02, (float)3.140000000e+02, (float)3.160000000e+02,
+      (float)3.180000000e+02, (float)3.230000000e+02, (float)3.260000000e+02,
+      (float)3.290000000e+02, (float)3.310000000e+02, (float)3.340000000e+02,
+      (float)3.370000000e+02, (float)3.410000000e+02, (float)3.430000000e+02,
+      (float)3.460000000e+02, (float)3.490000000e+02, (float)3.490000000e+02,
+      (float)3.510000000e+02, (float)3.530000000e+02, (float)3.570000000e+02,
+      (float)3.580000000e+02, (float)3.610000000e+02, (float)3.650000000e+02,
+      (float)3.680000000e+02, (float)3.710000000e+02, (float)3.740000000e+02,
+      (float)3.760000000e+02, (float)3.780000000e+02, (float)3.820000000e+02,
+      (float)3.850000000e+02, (float)3.880000000e+02, (float)3.910000000e+02,
+      (float)3.920000000e+02, (float)3.940000000e+02, (float)3.960000000e+02,
+      (float)3.990000000e+02, (float)4.010000000e+02, (float)4.030000000e+02,
+      (float)4.050000000e+02, (float)4.070000000e+02, (float)4.100000000e+02,
+      (float)4.140000000e+02, (float)4.160000000e+02, (float)4.190000000e+02,
+      (float)4.220000000e+02, (float)4.250000000e+02, (float)4.270000000e+02,
+      (float)4.320000000e+02, (float)4.350000000e+02, (float)4.380000000e+02,
+      (float)4.410000000e+02, (float)4.440000000e+02, (float)4.470000000e+02,
+      (float)4.500000000e+02, (float)4.520000000e+02, (float)4.540000000e+02,
+      (float)4.570000000e+02, (float)4.590000000e+02, (float)4.640000000e+02,
+      (float)4.670000000e+02, (float)4.700000000e+02, (float)4.720000000e+02,
+      (float)4.750000000e+02, (float)4.780000000e+02, (float)4.810000000e+02,
+      (float)4.840000000e+02, (float)4.870000000e+02, (float)4.890000000e+02,
+      (float)4.910000000e+02, (float)4.960000000e+02, (float)4.970000000e+02,
+      (float)5.000000000e+02, (float)5.030000000e+02, (float)5.060000000e+02,
+      (float)5.090000000e+02, (float)5.120000000e+02, (float)5.150000000e+02,
+      (float)5.170000000e+02, (float)5.220000000e+02, (float)5.250000000e+02,
+      (float)5.270000000e+02, (float)5.300000000e+02, (float)5.350000000e+02,
+      (float)5.390000000e+02, (float)5.420000000e+02, (float)5.470000000e+02,
+      (float)5.520000000e+02, (float)5.560000000e+02, (float)5.580000000e+02,
+      (float)5.630000000e+02, (float)5.670000000e+02, (float)5.700000000e+02,
+      (float)5.740000000e+02, (float)5.760000000e+02, (float)5.800000000e+02,
+      (float)5.830000000e+02, (float)5.880000000e+02, (float)5.910000000e+02,
+      (float)5.940000000e+02, (float)5.970000000e+02, (float)6.000000000e+02,
+      (float)6.030000000e+02, (float)6.060000000e+02, (float)6.100000000e+02,
+      (float)6.110000000e+02, (float)6.160000000e+02, (float)6.220000000e+02,
+      (float)6.240000000e+02, (float)6.270000000e+02, (float)6.300000000e+02,
+      (float)6.320000000e+02, (float)6.350000000e+02, (float)6.380000000e+02,
+      (float)6.410000000e+02, (float)6.430000000e+02, (float)6.450000000e+02,
+      (float)6.500000000e+02, (float)6.530000000e+02, (float)6.560000000e+02,
+      (float)6.590000000e+02, (float)6.630000000e+02, (float)6.670000000e+02,
+      (float)6.700000000e+02, (float)6.730000000e+02, (float)6.760000000e+02,
+      (float)6.790000000e+02, (float)6.850000000e+02, (float)6.890000000e+02,
+      (float)6.930000000e+02, (float)6.960000000e+02, (float)7.010000000e+02,
+      (float)7.030000000e+02, (float)7.050000000e+02, (float)7.090000000e+02,
+      (float)7.110000000e+02, (float)7.130000000e+02, (float)7.140000000e+02,
+      (float)7.180000000e+02, (float)7.200000000e+02, (float)7.220000000e+02,
+      (float)7.240000000e+02, (float)7.270000000e+02, (float)7.300000000e+02,
+      (float)7.320000000e+02, (float)7.340000000e+02, (float)7.370000000e+02,
+      (float)7.420000000e+02, (float)7.440000000e+02, (float)7.490000000e+02,
+      (float)7.520000000e+02, (float)7.570000000e+02, (float)7.590000000e+02,
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+      (float)3.600500000e+04, (float)3.602300000e+04, (float)3.603300000e+04};
+  llvm::emitc::Tensor<float, 999> v59 = {
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)5.000000000e+00, (float)5.000000000e+00, (float)5.000000000e+00,
+      (float)1.000000000e+01, (float)1.000000000e+01, (float)1.000000000e+01,
+      (float)1.500000000e+01, (float)2.000000000e+01, (float)3.000000000e+01};
+  llvm::emitc::Tensor<float, 9999> v60 = {
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00};
+  llvm::emitc::Tensor<float, 9999> v61 = {
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)9.000000000e+01, (float)1.450000000e+02,
+      (float)1.750000000e+02, (float)1.750000000e+02, (float)1.750000000e+02};
+  llvm::emitc::Tensor<float, 999> v62 = {
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+      (float)2.000000000e+01, (float)2.000000000e+01, (float)2.000000000e+01,
+      (float)2.000000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
+      (float)2.500000000e+01, (float)2.500000000e+01, (float)2.500000000e+01,
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+      (float)3.000000000e+01, (float)3.000000000e+01, (float)3.000000000e+01,
+      (float)3.000000000e+01, (float)3.000000000e+01, (float)3.000000000e+01,
+      (float)3.500000000e+01, (float)3.500000000e+01, (float)3.500000000e+01,
+      (float)3.500000000e+01, (float)3.500000000e+01, (float)3.500000000e+01,
+      (float)4.000000000e+01, (float)4.000000000e+01, (float)4.000000000e+01,
+      (float)4.000000000e+01, (float)4.000000000e+01, (float)4.500000000e+01,
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+      (float)5.500000000e+01, (float)5.500000000e+01, (float)5.500000000e+01,
+      (float)6.000000000e+01, (float)6.000000000e+01, (float)6.500000000e+01,
+      (float)7.000000000e+01, (float)7.500000000e+01, (float)8.000000000e+01,
+      (float)8.500000000e+01, (float)9.000000000e+01, (float)1.000000000e+02,
+      (float)1.050000000e+02, (float)1.150000000e+02, (float)1.300000000e+02,
+      (float)1.500000000e+02, (float)2.000000000e+02, (float)3.250000000e+02};
+  llvm::emitc::Tensor<float, 999> v63 = {
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+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00};
+  llvm::emitc::Tensor<float, 999> v64 = {
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+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)0.0e+00,        (float)0.0e+00,
+      (float)0.0e+00, (float)0.0e+00, (float)4.500000000e+01};
+  llvm::emitc::Tensor<float, 999> v65 = {
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00};
+  llvm::emitc::Tensor<float, 999> v66 = {
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+      (float)4.900000000e+01, (float)5.500000000e+01, (float)6.100000000e+01,
+      (float)6.600000000e+01, (float)7.100000000e+01, (float)7.500000000e+01,
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+      (float)9.200000000e+01, (float)9.600000000e+01, (float)1.000000000e+02,
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+      (float)1.250000000e+02, (float)1.290000000e+02, (float)1.320000000e+02,
+      (float)1.350000000e+02, (float)1.390000000e+02, (float)1.420000000e+02,
+      (float)1.450000000e+02, (float)1.480000000e+02, (float)1.520000000e+02,
+      (float)1.550000000e+02, (float)1.580000000e+02, (float)1.610000000e+02,
+      (float)1.640000000e+02, (float)1.670000000e+02, (float)1.710000000e+02,
+      (float)1.740000000e+02, (float)1.770000000e+02, (float)1.800000000e+02,
+      (float)1.830000000e+02, (float)1.860000000e+02, (float)1.890000000e+02,
+      (float)1.920000000e+02, (float)1.950000000e+02, (float)1.980000000e+02,
+      (float)2.010000000e+02, (float)2.040000000e+02, (float)2.070000000e+02,
+      (float)2.110000000e+02, (float)2.140000000e+02, (float)2.170000000e+02,
+      (float)2.200000000e+02, (float)2.230000000e+02, (float)2.260000000e+02,
+      (float)2.290000000e+02, (float)2.320000000e+02, (float)2.350000000e+02,
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00};
+  llvm::emitc::Tensor<float, 999> v68 = {
+      (float)-5.500000000e+01, (float)-5.500000000e+01, (float)-5.000000000e+01,
+      (float)-5.000000000e+01, (float)-5.000000000e+01, (float)-5.000000000e+01,
+      (float)-4.500000000e+01, (float)-4.500000000e+01, (float)-4.500000000e+01,
+      (float)-4.500000000e+01, (float)-4.500000000e+01, (float)-4.500000000e+01,
+      (float)-4.500000000e+01, (float)-4.500000000e+01, (float)-4.500000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-4.000000000e+01,
+      (float)-4.000000000e+01, (float)-4.000000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
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+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
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+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.500000000e+01, (float)-3.500000000e+01, (float)-3.500000000e+01,
+      (float)-3.000000000e+01, (float)-3.000000000e+01, (float)-3.000000000e+01,
+      (float)-3.000000000e+01, (float)-3.000000000e+01, (float)-3.000000000e+01,
+      (float)-3.000000000e+01, (float)-3.000000000e+01, (float)-3.000000000e+01,
+      (float)-3.000000000e+01, (float)-3.000000000e+01, (float)-3.000000000e+01,
+      (float)-3.000000000e+01, (float)-3.000000000e+01, (float)-3.000000000e+01,
+      (float)-3.000000000e+01, (float)-3.000000000e+01, (float)-3.000000000e+01,
+      (float)-3.000000000e+01, (float)-3.000000000e+01, (float)-3.000000000e+01,
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+      (float)-1.500000000e+01, (float)-1.500000000e+01, (float)-1.500000000e+01,
+      (float)-1.500000000e+01, (float)-1.500000000e+01, (float)-1.500000000e+01,
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+      (float)-1.500000000e+01, (float)-1.500000000e+01, (float)-1.500000000e+01,
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+      (float)6.500000000e+01,  (float)6.500000000e+01,  (float)6.500000000e+01,
+      (float)6.500000000e+01,  (float)6.500000000e+01,  (float)6.500000000e+01,
+      (float)6.500000000e+01,  (float)6.500000000e+01,  (float)7.000000000e+01,
+      (float)7.000000000e+01,  (float)7.000000000e+01,  (float)7.000000000e+01,
+      (float)7.000000000e+01,  (float)7.000000000e+01,  (float)7.500000000e+01,
+      (float)7.500000000e+01,  (float)8.000000000e+01,  (float)8.000000000e+01,
+      (float)8.000000000e+01,  (float)8.000000000e+01,  (float)8.500000000e+01,
+      (float)8.500000000e+01,  (float)9.000000000e+01,  (float)9.000000000e+01,
+      (float)9.000000000e+01,  (float)9.500000000e+01,  (float)9.500000000e+01,
+      (float)1.000000000e+02,  (float)1.000000000e+02,  (float)1.050000000e+02,
+      (float)1.100000000e+02,  (float)1.150000000e+02,  (float)1.150000000e+02,
+      (float)1.200000000e+02,  (float)1.250000000e+02,  (float)1.250000000e+02,
+      (float)1.250000000e+02,  (float)1.300000000e+02,  (float)1.300000000e+02,
+      (float)1.350000000e+02,  (float)1.400000000e+02,  (float)1.450000000e+02,
+      (float)1.500000000e+02,  (float)1.550000000e+02,  (float)1.550000000e+02,
+      (float)1.650000000e+02,  (float)1.650000000e+02,  (float)1.750000000e+02,
+      (float)1.800000000e+02,  (float)1.850000000e+02,  (float)1.950000000e+02,
+      (float)2.050000000e+02,  (float)2.100000000e+02,  (float)2.200000000e+02,
+      (float)2.300000000e+02,  (float)2.350000000e+02,  (float)2.350000000e+02,
+      (float)2.400000000e+02,  (float)2.500000000e+02,  (float)2.650000000e+02,
+      (float)2.800000000e+02,  (float)2.900000000e+02,  (float)3.050000000e+02,
+      (float)3.200000000e+02,  (float)3.400000000e+02,  (float)3.500000000e+02,
+      (float)3.700000000e+02,  (float)3.950000000e+02,  (float)4.200000000e+02,
+      (float)4.500000000e+02,  (float)4.800000000e+02,  (float)5.200000000e+02,
+      (float)5.550000000e+02,  (float)6.550000000e+02,  (float)7.700000000e+02,
+      (float)9.450000000e+02,  (float)1.220000000e+03,  (float)2.050000000e+03};
+  llvm::emitc::Tensor<float, 999> v69 = {
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)3.000000000e+00, (float)3.000000000e+00, (float)3.000000000e+00,
+      (float)4.000000000e+00, (float)4.000000000e+00, (float)4.000000000e+00,
+      (float)4.000000000e+00, (float)4.000000000e+00, (float)4.000000000e+00,
+      (float)4.000000000e+00, (float)4.000000000e+00, (float)4.000000000e+00,
+      (float)4.000000000e+00, (float)4.000000000e+00, (float)4.000000000e+00,
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+      (float)6.000000000e+00, (float)6.000000000e+00, (float)7.000000000e+00};
+  llvm::emitc::Tensor<float, 999> v70 = {
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+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+      (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
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+      (float)2.000000000e+01, (float)2.000000000e+01, (float)3.000000000e+01};
+  llvm::emitc::Tensor<float, 999> v71 = {
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+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
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+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
+      (float)2.000000000e+00, (float)2.000000000e+00, (float)2.000000000e+00,
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+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
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+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
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+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.500000000e+01,
+                            (float)-3.500000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01, (float)-3.000000000e+01,
+                            (float)-3.000000000e+01};
+  llvm::emitc::Tensor<float, 999> v73 = {
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
+      (float)1.000000000e+00, (float)1.000000000e+00, (float)1.000000000e+00,
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+  llvm::emitc::Tensor<float, 40, 71> v75 = {
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+      (float)-4.461074250e-02, (float)6.361674520e-03,  (float)-1.073772530e-01,
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+      (float)-1.074918960e-01, (float)2.743638160e-01,  (float)-1.093075130e-01,
+      (float)1.594399960e-01,  (float)1.913003180e-01,  (float)-5.677055190e-02,
+      (float)-9.528003630e-02, (float)6.295419480e-02,  (float)-7.568315860e-01,
+      (float)-1.526631710e+00, (float)-9.417661270e-02, (float)2.335946080e-02,
+      (float)-6.538580360e-02, (float)1.125368180e-01,  (float)2.942163940e-01,
+      (float)-2.647621040e-01, (float)-3.990438580e-01, (float)3.700554070e-01,
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+      (float)3.419344130e-01,  (float)-2.675497720e-02, (float)-4.148540090e-03,
+      (float)3.243315220e-02,  (float)-8.096088470e-02, (float)1.014248210e-01,
+      (float)-4.912083150e-01, (float)-9.071546790e-02, (float)4.800837340e-01,
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+      (float)-1.598779560e-01, (float)2.076133040e-01,  (float)-2.023130060e-01,
+      (float)-1.424104420e-01, (float)7.045190040e-02,  (float)-2.361834050e-03,
+      (float)-5.830025300e-02, (float)-1.408982570e-01, (float)3.562102470e-02,
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+      (float)-5.225165190e-02, (float)-2.332762330e-01, (float)-1.396791340e-01,
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+      (float)-1.125051010e-01, (float)-2.747814060e-01, (float)3.444011320e-03,
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+      (float)-1.221001890e-01, (float)1.547835770e-01,  (float)-1.937162430e-01,
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+      (float)6.352230900e-02,  (float)1.069874170e-01,  (float)1.094375850e-01,
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+      (float)9.657496950e-02,  (float)1.270195390e-01,  (float)1.200804190e-01,
+      (float)-2.970583440e-01, (float)1.386448000e-01,  (float)2.118947540e-02,
+      (float)-2.465196890e-02, (float)2.977587280e-01,  (float)-1.252066340e-01,
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+      (float)-6.359713520e-02, (float)2.326733770e-01,  (float)1.548719850e-01,
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+      (float)2.686094050e-01,  (float)5.316223500e-01,  (float)2.080051750e-01,
+      (float)2.699769140e-01,  (float)-9.852997210e-02, (float)-4.078891280e-01,
+      (float)-2.551985980e-01, (float)5.084387660e-01,  (float)2.509025930e-01,
+      (float)1.640245320e-02,  (float)-4.703008530e-01, (float)-9.260824320e-02,
+      (float)-5.503688750e-02, (float)2.040181460e-01,  (float)-2.361569740e-02,
+      (float)-8.834089340e-02, (float)5.619591570e-03,  (float)3.549554570e-02,
+      (float)2.189213480e-01,  (float)9.615762530e-02,  (float)-7.087387140e-02,
+      (float)1.687772130e-01,  (float)-2.864357830e-01, (float)-1.966226990e-01,
+      (float)1.130231380e-02,  (float)6.525266170e-02,  (float)5.892705170e-02,
+      (float)-2.163160590e-01, (float)1.408587690e-01,  (float)3.478549050e-02,
+      (float)2.693522270e-01,  (float)2.131671910e-01,  (float)5.083147810e-02,
+      (float)-2.390407030e-01, (float)-9.028849000e-02, (float)-7.455941440e-01,
+      (float)-1.594309450e+00, (float)-5.106388400e-02, (float)1.461232600e-01,
+      (float)1.987238230e-02,  (float)3.771908120e-03,  (float)-1.543489750e-03,
+      (float)-1.507417860e-01, (float)-4.076465070e-01, (float)1.273370980e-01,
+      (float)-4.912498590e-02, (float)4.803178460e-02,  (float)1.381325270e-01,
+      (float)-5.507323890e-02, (float)-7.885853950e-02, (float)-1.249458790e-01,
+      (float)-3.100775740e-02, (float)-2.126214950e-01, (float)6.567206230e-02,
+      (float)-2.859816890e-02, (float)-9.255159490e-01, (float)3.894067700e-01,
+      (float)2.304976280e-01,  (float)9.553889930e-02,  (float)9.168382730e-02,
+      (float)-1.055516300e-01, (float)9.530881790e-02,  (float)-2.193289400e-01,
+      (float)1.900171490e-01,  (float)-1.615601030e-01, (float)6.596876680e-02,
+      (float)2.177739290e-01,  (float)-3.728242220e-01, (float)-3.038952690e-02,
+      (float)-2.780113180e-02, (float)7.955130930e-02,  (float)3.264623880e-01,
+      (float)-1.261066650e-01, (float)4.470206200e-01,  (float)1.012141850e-01,
+      (float)-1.109225090e-03, (float)-6.851707700e-01, (float)-5.112677810e-01,
+      (float)1.696628480e-01,  (float)1.134194360e-01,  (float)-1.693052650e-01,
+      (float)-1.970619110e-01, (float)-1.007431520e-01, (float)1.360448750e-01,
+      (float)5.298155920e-02,  (float)1.596349330e-01,  (float)2.267163840e-01,
+      (float)-1.719156650e-01, (float)1.316127090e-02,  (float)-1.181333880e-01,
+      (float)2.931635450e-02,  (float)1.478746380e-01,  (float)-2.496959270e-01,
+      (float)-9.192533040e-02, (float)7.494027910e-02,  (float)3.000770810e-01,
+      (float)-3.081419690e-02, (float)9.794940790e-02,  (float)2.203881000e-01,
+      (float)-3.384236400e-01, (float)-4.231948410e-02, (float)1.678591590e-02,
+      (float)3.896306750e-01,  (float)-2.614724930e-01, (float)1.072446410e-01,
+      (float)1.406998190e-01,  (float)-2.468102430e-01, (float)-1.916948710e-01,
+      (float)-3.395185990e-02, (float)-3.481206600e-01, (float)-1.367874150e-01,
+      (float)-2.418266530e-01, (float)1.167153420e-01,  (float)1.958039550e-01,
+      (float)1.840713320e-01,  (float)1.892721800e-01,  (float)2.498452090e-03,
+      (float)-9.489145870e-02, (float)-9.936349830e-02, (float)-2.924395730e-02,
+      (float)1.491950010e-01,  (float)-2.409518360e-01, (float)-1.630719750e-02,
+      (float)-1.057339910e-01, (float)-3.691458700e-01, (float)-4.757694300e-01,
+      (float)-1.767502130e-01, (float)-3.084469140e-01, (float)1.155899610e-01,
+      (float)4.854257780e-02,  (float)-1.438768510e-01, (float)4.997157310e-02,
+      (float)-2.142907530e-01, (float)7.421793040e-02,  (float)1.740921290e-01,
+      (float)1.417245420e-01,  (float)-1.688472630e-01, (float)-9.464255720e-02,
+      (float)-1.334459190e-01, (float)-2.065002320e-01, (float)2.672322390e-01,
+      (float)-1.996624470e-01, (float)-1.139759790e-01, (float)1.002959460e-01,
+      (float)-5.694196220e-01, (float)-6.233651940e-02, (float)-2.353912440e-01,
+      (float)-7.087327540e-02, (float)-5.249817970e-01};
+  llvm::emitc::Tensor<float, 40> v76 = {
+      (float)-1.639103000e-01, (float)6.592668360e-04,  (float)1.320010980e-02,
+      (float)-1.435504260e-01, (float)-3.149139230e-03, (float)4.562379790e-02,
+      (float)4.029012470e-02,  (float)4.505958780e-02,  (float)5.873173940e-03,
+      (float)-1.699868590e-01, (float)-1.416285190e-01, (float)1.757070050e-02,
+      (float)-1.492233130e-01, (float)-2.476427700e-01, (float)-5.176663030e-02,
+      (float)-6.201457230e-02, (float)-1.438054590e-01, (float)-4.845643790e-02,
+      (float)-6.979651000e-02, (float)1.925524670e-03,  (float)9.662723540e-02,
+      (float)-6.584911050e-02, (float)8.369737300e-03,  (float)-1.696964170e-02,
+      (float)-7.050643860e-02, (float)-1.311026220e-01, (float)5.035200720e-02,
+      (float)8.589960630e-02,  (float)4.521270470e-02,  (float)-4.689359660e-02,
+      (float)1.544586570e-01,  (float)-1.906073090e-02, (float)1.417852340e-01,
+      (float)1.148017940e-03,  (float)1.224328880e-01,  (float)1.765405390e-01,
+      (float)3.348778360e-06,  (float)1.071403250e-01,  (float)3.681195530e-02,
+      (float)4.506632310e-02};
+  llvm::emitc::Tensor<float, 40, 40> v77 = {
+      (float)1.942449210e-01,  (float)2.699950040e-01,  (float)4.142592850e-01,
+      (float)-4.258062540e-01, (float)1.419910040e-01,  (float)1.994077560e-01,
+      (float)-1.153015870e-01, (float)3.618899290e-01,  (float)-1.060230660e-01,
+      (float)4.024399520e-01,  (float)3.860437270e-01,  (float)2.751367390e-01,
+      (float)1.038137730e-02,  (float)-3.592236340e-02, (float)1.638590100e-01,
+      (float)2.557660940e-01,  (float)2.976976630e-01,  (float)-1.651686880e-01,
+      (float)-9.972485150e-02, (float)1.581665690e-02,  (float)-4.128591720e-01,
+      (float)-9.207004300e-02, (float)-1.051898150e-01, (float)-2.062526200e-01,
+      (float)1.718897070e-01,  (float)-3.890019950e-01, (float)6.370367110e-02,
+      (float)7.574949410e-02,  (float)3.609710340e-01,  (float)-3.163828330e-02,
+      (float)-5.503930450e-01, (float)-2.149164680e-01, (float)6.653308870e-02,
+      (float)-1.785505560e-01, (float)6.102755660e-02,  (float)-3.485923110e-01,
+      (float)1.035364720e-02,  (float)-3.618145880e-01, (float)-2.147822830e-02,
+      (float)1.395274850e-01,  (float)-2.350635680e-01, (float)1.072513780e-02,
+      (float)-3.723135590e-01, (float)-4.581001700e-01, (float)7.096004490e-02,
+      (float)1.137864660e-01,  (float)2.349664570e-01,  (float)6.278925390e-02,
+      (float)3.167586920e-01,  (float)1.520434170e-01,  (float)-1.194689650e-01,
+      (float)-3.593593090e-02, (float)4.511301820e-01,  (float)-1.195040870e-01,
+      (float)-2.202107160e-01, (float)7.270702710e-02,  (float)4.317052660e-01,
+      (float)1.325534280e-01,  (float)3.964829440e-02,  (float)4.453346730e-01,
+      (float)2.372169490e-01,  (float)1.412442480e-01,  (float)-2.778489290e-01,
+      (float)-2.191730890e-01, (float)-8.195573830e-02, (float)-3.796201350e-01,
+      (float)-1.054622080e-01, (float)-5.113612410e-01, (float)2.309326640e-02,
+      (float)4.202780720e-01,  (float)3.718819100e-02,  (float)9.860849940e-03,
+      (float)-1.884679350e-01, (float)-7.152913810e-01, (float)1.889725630e-01,
+      (float)-6.605301490e-01, (float)5.560345580e-02,  (float)-1.281323580e-01,
+      (float)-4.881768670e-02, (float)1.820913260e-01,  (float)6.102971730e-02,
+      (float)1.132690160e-01,  (float)-4.465542440e-01, (float)2.067211270e-01,
+      (float)-2.229362280e-01, (float)3.329693380e-01,  (float)3.144160210e-01,
+      (float)-9.518109260e-02, (float)-2.233716550e-01, (float)-4.426179530e-01,
+      (float)-4.443724750e-01, (float)3.920993210e-02,  (float)-6.307611470e-01,
+      (float)-1.378714480e-02, (float)2.544815840e-01,  (float)7.252320640e-02,
+      (float)-4.951232370e-01, (float)1.519222710e-01,  (float)-1.876245290e-01,
+      (float)5.465682150e-01,  (float)6.630297900e-01,  (float)-2.195028220e-02,
+      (float)-6.870272010e-02, (float)1.084284660e+00,  (float)-2.855912150e-01,
+      (float)-4.705257420e-01, (float)3.925424520e-01,  (float)-2.913774250e-01,
+      (float)-7.008304450e-02, (float)-1.851098240e-01, (float)6.871560810e-01,
+      (float)5.611283180e-01,  (float)-6.889331340e-02, (float)3.500039280e-01,
+      (float)-4.436692300e-01, (float)6.122646330e-01,  (float)-3.933624330e-01,
+      (float)9.016789190e-01,  (float)8.399136660e-01,  (float)-2.054832880e-01,
+      (float)3.854021730e-01,  (float)4.064182190e-02,  (float)1.270509510e-01,
+      (float)4.268527030e-01,  (float)1.073798980e-01,  (float)4.924513400e-01,
+      (float)-8.227632940e-02, (float)-2.945718170e-01, (float)6.800666450e-02,
+      (float)4.520421620e-01,  (float)-2.945373020e-02, (float)-1.882228850e-01,
+      (float)5.203092690e-01,  (float)1.106640470e-01,  (float)1.424926370e-01,
+      (float)-1.562246380e-01, (float)9.395793820e-02,  (float)1.939959380e-01,
+      (float)1.555379480e-01,  (float)-9.332291780e-02, (float)-3.667893710e-01,
+      (float)5.395315290e-01,  (float)1.269005980e-01,  (float)-6.406446700e-01,
+      (float)4.151482580e-01,  (float)-1.118060280e-01, (float)-3.865232170e-01,
+      (float)2.712691130e-01,  (float)3.681036530e-01,  (float)1.643330160e-01,
+      (float)-7.217261190e-01, (float)-1.435617950e-01, (float)-2.978720660e-01,
+      (float)-7.709093090e-01, (float)5.388501660e-02,  (float)-6.034669880e-01,
+      (float)-3.294746280e-01, (float)-2.076891660e-01, (float)1.184282750e-01,
+      (float)2.229107730e-02,  (float)-1.922440830e-01, (float)2.888987360e-01,
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+      (float)5.980577470e-01,  (float)7.924775030e-02,  (float)8.674179910e-01,
+      (float)5.748869780e-01,  (float)3.277497740e-02,  (float)-2.482161070e-01,
+      (float)2.994290740e-02,  (float)6.414513580e-01,  (float)1.721007750e-02,
+      (float)3.899632020e-02,  (float)3.988329470e-01,  (float)-5.409405830e-01,
+      (float)-3.129498360e-01, (float)-3.191410010e-01, (float)4.983655210e-01,
+      (float)-2.620849910e-01, (float)2.612000410e-01,  (float)3.455328350e-01,
+      (float)9.971060600e-02,  (float)2.036804410e-01,  (float)9.912937870e-02,
+      (float)-8.634011260e-03, (float)-2.889369130e-01, (float)-3.032090960e-01,
+      (float)-1.930381510e-01, (float)-2.314196710e-02, (float)-6.841212510e-02,
+      (float)3.458260000e-01,  (float)-3.570681210e-01, (float)-1.042376680e-01,
+      (float)-4.990408570e-02, (float)-4.034003240e-02, (float)8.925898370e-02,
+      (float)6.285914030e-02,  (float)8.433468640e-03,  (float)-5.354915260e-01,
+      (float)-4.686858360e-01, (float)-2.323784530e-01, (float)-1.348145310e-01,
+      (float)-2.836185100e-01, (float)-2.628516260e-01, (float)1.026675850e-01,
+      (float)-2.993393920e-03, (float)-2.772897670e-02, (float)-2.004349980e-01,
+      (float)1.245284600e-01,  (float)-9.579234570e-02, (float)-1.699635390e-01,
+      (float)2.950907350e-01,  (float)-2.232146560e-01, (float)-1.390507820e-01,
+      (float)-1.921251040e-02, (float)-4.031083290e-01, (float)-3.678480400e-03,
+      (float)7.372565260e-01,  (float)-3.252608780e-01, (float)-1.244889200e-01,
+      (float)4.244728680e-01,  (float)-1.307673750e-01, (float)1.424884500e-01,
+      (float)2.290245150e-01,  (float)-2.202557210e-02, (float)-9.576227510e-03,
+      (float)-3.014218210e-01, (float)-3.703987600e-02, (float)-1.876098220e-01,
+      (float)3.973548110e-01,  (float)1.437912430e-01,  (float)-1.793007400e-01,
+      (float)3.002218310e-01,  (float)-5.496347700e-02, (float)9.244085100e-02,
+      (float)1.339629590e-01,  (float)-1.647304740e-02, (float)3.581428830e-01,
+      (float)-5.763772730e-01, (float)4.705319930e-02,  (float)2.725977000e-01,
+      (float)-3.605936770e-01, (float)5.501571300e-01,  (float)1.416553560e-01,
+      (float)3.649396600e-01,  (float)-6.929839850e-01, (float)-2.964030500e-01,
+      (float)-3.548767570e-01, (float)3.104901020e-01,  (float)-3.680630620e-01,
+      (float)2.187407950e-02,  (float)-2.159471810e-01, (float)-3.702240290e-01,
+      (float)-2.369479690e-01, (float)-4.812089730e-02, (float)1.187781170e-01,
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+      (float)5.900743980e-02,  (float)3.831789790e-01,  (float)7.287064940e-02,
+      (float)-3.071362970e-01, (float)1.175340120e-01,  (float)-1.422658560e-01,
+      (float)1.609118130e-01,  (float)-1.628430630e-01, (float)2.038966270e-01,
+      (float)-1.139387710e-01, (float)1.547322870e-01,  (float)-1.946154680e-01,
+      (float)1.691389980e-01,  (float)2.382975220e-01,  (float)2.909688950e-01,
+      (float)-8.043034370e-02, (float)1.552370940e-01,  (float)3.168898820e-01,
+      (float)-5.464854840e-02, (float)-5.223595500e-01, (float)-1.535858060e-01,
+      (float)-1.730464250e-01, (float)-3.094758390e-01, (float)1.373959930e-01,
+      (float)-8.804437510e-02, (float)-4.562943430e-02, (float)-1.716365670e-01,
+      (float)-2.058297990e-01, (float)-3.545438500e-02, (float)1.383809600e-01,
+      (float)4.304621820e-01,  (float)5.257873540e-01,  (float)-5.028883460e-03,
+      (float)-3.168755170e-01, (float)3.213191030e-02,  (float)-3.037697080e-01,
+      (float)-2.769875820e-01, (float)2.608229220e-01,  (float)1.708934610e-01,
+      (float)-3.142875390e-03, (float)4.727781710e-01,  (float)4.856695230e-01,
+      (float)4.907641410e-01,  (float)1.235561220e-01,  (float)3.758164350e-01,
+      (float)4.952664970e-01,  (float)2.117855400e-01,  (float)1.061255860e-01,
+      (float)2.076992390e-01,  (float)-2.762619260e-01, (float)3.531170640e-02,
+      (float)1.505434070e-01,  (float)-9.042925380e-02, (float)2.598064840e-01,
+      (float)-2.914900780e-01, (float)3.756931130e-01,  (float)-1.393014010e-02,
+      (float)4.013363120e-01,  (float)-1.861221940e-01, (float)-1.352545020e-01,
+      (float)-3.804022970e-01, (float)-4.288584890e-01, (float)-1.083881870e-02,
+      (float)3.079023960e-01,  (float)-1.148406120e-01, (float)-9.533207120e-02,
+      (float)-3.998994530e-01, (float)-5.868433420e-02, (float)-4.778203670e-01,
+      (float)9.297312800e-02,  (float)-2.239654360e-01, (float)-3.211522400e-01,
+      (float)-3.912535310e-01, (float)6.946559250e-02,  (float)-2.540934370e-03,
+      (float)-3.039481640e-01, (float)-2.928518060e-01, (float)3.960106670e-01,
+      (float)5.765932200e-01,  (float)1.803074330e-01,  (float)-1.074663850e-01,
+      (float)4.825291340e-01,  (float)-2.015585450e-01, (float)-2.927610870e-01,
+      (float)-5.265598890e-01, (float)1.223778580e-01,  (float)1.226894410e-01,
+      (float)4.618513580e-02,  (float)-8.437137310e-02, (float)-7.610270380e-02,
+      (float)1.009650080e-01,  (float)-5.309196110e-01, (float)-4.013866780e-01,
+      (float)2.172307220e-01,  (float)3.686127360e-01,  (float)8.526873580e-02,
+      (float)-2.353149350e-01, (float)-4.175347090e-01, (float)-7.631525390e-02,
+      (float)-2.310011830e-01, (float)-2.627640960e-01, (float)-3.293836710e-01,
+      (float)-3.611749710e-01, (float)1.723274890e-01,  (float)-4.053583740e-02,
+      (float)4.278765020e-01,  (float)-3.415701390e-01, (float)-1.606933920e-01,
+      (float)1.335840210e-02,  (float)-1.092915240e-01, (float)-3.588020210e-01,
+      (float)1.824383620e-02,  (float)-2.949094470e-01, (float)-3.923834490e-02,
+      (float)-1.368585040e-02, (float)8.105469160e-04,  (float)1.503808650e-01,
+      (float)-1.114018860e-01, (float)7.752321660e-02,  (float)-3.324640100e-01,
+      (float)-1.510917250e-01, (float)-1.239486340e-01, (float)1.502818910e-01,
+      (float)1.027048900e-01,  (float)4.140413110e-01,  (float)-2.207781670e-01,
+      (float)-5.697364210e-01, (float)4.115386900e-01,  (float)-1.449839770e-01,
+      (float)-2.439443770e-01, (float)-1.871379910e-01, (float)8.004081250e-02,
+      (float)-5.373737960e-02, (float)5.550503360e-02,  (float)-8.746340120e-02,
+      (float)-9.091094880e-02, (float)-4.467231330e-01, (float)1.897434290e-01,
+      (float)8.179691430e-02,  (float)-5.886338650e-02, (float)4.932152030e-01,
+      (float)2.994157970e-01,  (float)2.485867030e-02,  (float)-6.046364080e-02,
+      (float)2.315392050e-01,  (float)-4.449968930e-01, (float)-4.636459050e-01,
+      (float)1.261346640e-01,  (float)-3.916265370e-01, (float)-3.030861620e-01,
+      (float)5.057120700e-02,  (float)-1.182031330e-01, (float)1.834850160e-01,
+      (float)3.300078510e-01,  (float)-2.727780940e-01, (float)-2.660522760e-01,
+      (float)-4.256767630e-01, (float)-4.133676370e-02, (float)-5.124505760e-01,
+      (float)4.392443220e-02,  (float)-2.372299280e-01, (float)-2.392111270e-01,
+      (float)-4.838627950e-02, (float)-3.187938030e-01, (float)-3.255964520e-01,
+      (float)-2.226442690e-01, (float)3.191383180e-01,  (float)-1.011829900e-01,
+      (float)-1.182488430e-01, (float)2.855046390e-01,  (float)-3.809584980e-01,
+      (float)-2.217081490e-01, (float)9.976781600e-01,  (float)-1.775318830e-01,
+      (float)-1.410277630e-01, (float)3.751910630e-01,  (float)-4.443455490e-02,
+      (float)5.379760860e-01,  (float)1.493369940e-01,  (float)9.196971650e-01,
+      (float)8.073859810e-01,  (float)3.046056930e-01,  (float)7.747039790e-01,
+      (float)-1.567902420e-01, (float)7.600454680e-01,  (float)4.112594130e-01,
+      (float)1.125255350e+00,  (float)4.929722250e-01,  (float)3.548883500e-01,
+      (float)2.904264930e-01,  (float)-1.614922880e-01, (float)-1.503639070e-01,
+      (float)-1.393928380e-01, (float)4.342832570e-01,  (float)-3.690794710e-01,
+      (float)-1.386622190e-01, (float)8.168677240e-02,  (float)2.405175270e-01,
+      (float)6.780930160e-01,  (float)-2.171424930e-01, (float)1.136025190e-01,
+      (float)5.131943230e-01,  (float)-9.567520020e-02, (float)3.116578160e-01,
+      (float)2.337307630e-01,  (float)4.133338030e-01,  (float)-4.570401460e-02,
+      (float)3.914314880e-02,  (float)-3.912191990e-01, (float)-1.623469740e-01,
+      (float)-1.017762570e-01, (float)-1.178017710e-01, (float)-6.362856630e-01,
+      (float)2.544219790e-01,  (float)1.152533220e-01,  (float)-3.874690230e-01,
+      (float)-5.722415820e-02, (float)1.254853010e-01,  (float)-1.262966250e-01,
+      (float)-6.240930560e-01, (float)-2.011558230e-02, (float)-8.977451360e-03,
+      (float)-1.515659240e-01, (float)1.289531290e-01,  (float)-3.408088810e-03,
+      (float)-3.999197780e-01, (float)-5.212202070e-01, (float)-4.099874500e-01,
+      (float)3.510309160e-01};
+  llvm::emitc::Tensor<float, 40> v78 = {
+      (float)-1.178889200e-01, (float)-7.166976480e-02, (float)9.469684950e-02,
+      (float)-1.445470010e-01, (float)-3.123975130e-03, (float)-3.207838540e-02,
+      (float)-4.818724470e-02, (float)1.646877970e-01,  (float)7.505343850e-02,
+      (float)-3.706194460e-01, (float)-1.411788460e-01, (float)9.024682640e-02,
+      (float)5.108462650e-02,  (float)1.548991770e-03,  (float)-9.621844440e-02,
+      (float)1.285586950e-01,  (float)-2.410823290e-02, (float)-1.732027380e-01,
+      (float)-2.980469260e-03, (float)-2.746207450e-02, (float)-7.338333120e-02,
+      (float)7.750679550e-02,  (float)-9.884561590e-02, (float)-6.113795940e-02,
+      (float)1.979543830e-03,  (float)-1.246113330e-01, (float)-1.659987420e-01,
+      (float)1.889260110e-02,  (float)2.075578270e-02,  (float)-1.612419040e-01,
+      (float)9.697502850e-02,  (float)-1.243985070e-02, (float)1.113790180e-02,
+      (float)9.094715120e-02,  (float)-3.939445320e-02, (float)-1.471216140e-02,
+      (float)-1.097704920e-01, (float)-3.662016620e-02, (float)-5.321359630e-02,
+      (float)-2.992451000e-02};
+  llvm::emitc::Tensor<float, 20, 40> v79 = {
+      (float)2.324826120e-01,  (float)4.423185590e-01,  (float)-6.422753930e-01,
+      (float)3.244025410e-01,  (float)-9.001461860e-01, (float)4.397654530e-02,
+      (float)-6.331687560e-01, (float)-4.566561580e-01, (float)4.152465460e-01,
+      (float)6.237373500e-02,  (float)1.943661720e-01,  (float)-6.510128970e-01,
+      (float)-8.054086560e-01, (float)8.615653960e-02,  (float)-3.757489030e-01,
+      (float)-7.563986770e-01, (float)-4.075850840e-01, (float)-9.894760250e-01,
+      (float)2.588506340e-01,  (float)-6.503199930e-01, (float)-9.132218950e-01,
+      (float)1.752474760e-01,  (float)-6.761729120e-01, (float)-1.781409080e-01,
+      (float)-8.754683130e-01, (float)-3.155371550e-01, (float)-1.492173370e-01,
+      (float)-8.847839830e-01, (float)-9.392511240e-01, (float)-6.947209840e-01,
+      (float)-1.108319160e+00, (float)-8.420373200e-01, (float)-2.669231000e-01,
+      (float)4.459361140e-01,  (float)3.089661600e-01,  (float)4.081283510e-01,
+      (float)1.561680900e-02,  (float)-1.087309940e-01, (float)-6.793882250e-01,
+      (float)6.133446090e-01,  (float)-3.371148710e-01, (float)2.954251470e-01,
+      (float)4.181334380e-01,  (float)-2.464103700e-01, (float)4.335406420e-01,
+      (float)-4.303614200e-01, (float)2.310755700e-01,  (float)-6.935538350e-02,
+      (float)-1.214036790e-01, (float)-1.174380060e+00, (float)1.281708480e-01,
+      (float)2.461301830e-01,  (float)-4.398898780e-02, (float)1.511752010e-01,
+      (float)1.924597470e-01,  (float)5.399318340e-01,  (float)-1.662396640e-02,
+      (float)4.873437290e-01,  (float)-6.560725720e-02, (float)6.125664710e-01,
+      (float)1.889365760e-01,  (float)5.919666290e-01,  (float)1.116168800e-01,
+      (float)2.509434240e-03,  (float)1.889786310e-02,  (float)-4.752775130e-01,
+      (float)2.824106220e-01,  (float)1.122731340e-01,  (float)2.481019500e-01,
+      (float)4.600709970e-01,  (float)-8.617801210e-02, (float)2.720093130e-01,
+      (float)-4.825113420e-01, (float)-3.438760640e-01, (float)5.936656150e-02,
+      (float)-1.988838460e-01, (float)-1.488202810e-01, (float)2.205355320e-01,
+      (float)-2.022305500e-02, (float)-4.901379350e-01, (float)-9.745720770e-02,
+      (float)4.096868930e-01,  (float)-2.695048150e-01, (float)2.321245150e-02,
+      (float)-1.899362360e-01, (float)6.079664820e-01,  (float)-5.907611180e-02,
+      (float)-1.673513950e-01, (float)-3.429774190e-02, (float)-2.488902660e-01,
+      (float)-6.938240680e-02, (float)-4.285359080e-01, (float)-2.083116620e-01,
+      (float)4.118163880e-01,  (float)-5.225126820e-02, (float)-4.734675880e-01,
+      (float)3.442902270e-01,  (float)-9.403176610e-02, (float)8.893346190e-01,
+      (float)-4.173539280e-01, (float)-2.122265400e-01, (float)-5.188030000e-01,
+      (float)-1.894895580e-01, (float)-5.803297160e-01, (float)-3.095836040e-01,
+      (float)2.361140770e-02,  (float)5.202793480e-01,  (float)-4.888663290e-01,
+      (float)-2.090858820e-01, (float)-2.685896460e-01, (float)-3.056805130e-01,
+      (float)-2.994485800e-01, (float)1.515579040e-02,  (float)4.314621090e-01,
+      (float)2.908901570e-01,  (float)1.098286580e-01,  (float)7.335432770e-01,
+      (float)-1.734738800e-01, (float)1.990545240e-01,  (float)3.992945850e-01,
+      (float)-1.811009500e-01, (float)-2.546049360e-01, (float)4.385700820e-01,
+      (float)-2.814278600e-01, (float)3.723647590e-01,  (float)4.236407210e-02,
+      (float)7.194663580e-02,  (float)3.446313140e-01,  (float)6.513412300e-02,
+      (float)-7.094472050e-01, (float)2.217652200e-01,  (float)-4.799939320e-02,
+      (float)3.273808060e-01,  (float)-7.246454810e-02, (float)1.514499780e-01,
+      (float)4.774266180e-01,  (float)-4.686284360e-01, (float)1.437448860e-01,
+      (float)-1.791310910e-01, (float)3.359604180e-01,  (float)2.387392970e-01,
+      (float)3.477002080e-01,  (float)4.094595910e-01,  (float)2.238611430e-01,
+      (float)-1.714372260e-02, (float)-2.178403590e-01, (float)-2.448340510e-02,
+      (float)1.029436660e-01,  (float)2.107671090e-02,  (float)2.622194890e-01,
+      (float)1.255129870e-01,  (float)3.841022550e-01,  (float)-2.436068210e-01,
+      (float)-1.270672680e-01, (float)-5.499696140e-01, (float)1.089543480e-01,
+      (float)-2.309346570e-02, (float)9.545212230e-02,  (float)4.590747950e-01,
+      (float)-1.522184910e-01, (float)2.904289400e-02,  (float)-8.818178620e-02,
+      (float)2.504534420e-01,  (float)-4.311323170e-01, (float)3.820586800e-01,
+      (float)2.177117170e-01,  (float)-2.592618200e-02, (float)3.963339330e-01,
+      (float)-2.809675340e-01, (float)-1.065918560e+00, (float)2.971138060e-01,
+      (float)-5.723818760e-02, (float)-9.502319990e-02, (float)-1.255047770e-01,
+      (float)2.269118730e-01,  (float)2.756102380e-01,  (float)-1.101630480e-01,
+      (float)2.465447930e-01,  (float)-3.756073710e-01, (float)4.939568940e-01,
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+      (float)-5.132936840e-01, (float)2.942183320e-01,  (float)-6.713371870e-01,
+      (float)3.467343150e-01,  (float)-8.142403360e-01, (float)-7.736653680e-01,
+      (float)-2.264990810e-01, (float)-6.483533380e-01, (float)-1.229846920e-01,
+      (float)-8.217471830e-01, (float)2.703226510e-01,  (float)3.513719140e-02,
+      (float)-6.510109300e-01, (float)-6.906598810e-01, (float)-3.688707050e-01,
+      (float)-9.579101800e-01, (float)-8.507568840e-01, (float)3.010917310e-01,
+      (float)1.975249650e-01,  (float)7.201362250e-01,  (float)-3.384157720e-01,
+      (float)2.696233690e-01,  (float)-2.802918260e-01, (float)-2.319679410e-01,
+      (float)3.688238260e-01,  (float)-2.333278510e-01, (float)-2.279450000e-01,
+      (float)6.323163210e-02,  (float)-1.206192450e-01, (float)6.161751740e-01,
+      (float)-4.157950280e-01, (float)1.136372310e-01,  (float)1.284490530e-01,
+      (float)-1.384648680e-01, (float)-1.306738380e+00, (float)1.065198780e-01,
+      (float)2.660389840e-01,  (float)1.070073770e-01,  (float)-4.503095150e-02,
+      (float)2.657091620e-01,  (float)4.724887310e-01,  (float)-8.700073510e-02,
+      (float)1.551671330e-01,  (float)-4.270904360e-01, (float)1.554446670e-01,
+      (float)-1.092808620e-01, (float)2.424404320e-01,  (float)3.407474760e-01,
+      (float)-1.693004070e-01, (float)3.992065190e-01,  (float)-5.221441980e-01,
+      (float)1.327640940e-02,  (float)3.855528530e-01,  (float)-1.129992530e-01,
+      (float)5.407613140e-02,  (float)2.742832600e-01,  (float)2.237539890e-01,
+      (float)3.193411830e-01,  (float)-4.487538930e-01, (float)-1.554405880e-02,
+      (float)-2.143167260e-01, (float)-1.213760300e-01, (float)1.527040900e-01,
+      (float)1.256113500e-01,  (float)4.726113750e-02};
+  llvm::emitc::Tensor<float, 20> v80 = {
+      (float)3.355367780e-01, (float)-1.388000880e-02, (float)6.751911040e-01,
+      (float)2.667974220e-03, (float)1.055345460e-01,  (float)3.203891220e-03,
+      (float)6.174200770e-01, (float)-1.178330560e-02, (float)-1.767182650e-01,
+      (float)5.671358110e-02, (float)4.362215700e-01,  (float)-1.028030290e-01,
+      (float)9.947001180e-02, (float)5.265628100e-01,  (float)6.506712440e-01,
+      (float)4.370287660e-01, (float)7.621463530e-01,  (float)5.977262850e-01,
+      (float)4.100183840e-01, (float)-1.327016650e-01};
+  llvm::emitc::Tensor<float, 2, 20> v81 = {
+      (float)7.043257950e-01,  (float)-9.608286610e-02, (float)5.232187510e-01,
+      (float)-1.858599630e-01, (float)-1.736247840e-01, (float)-3.508510590e-01,
+      (float)4.727205340e-01,  (float)-1.409519020e-01, (float)-2.538996040e-01,
+      (float)-1.509172770e-01, (float)4.717685280e-01,  (float)-3.412884770e-01,
+      (float)-1.333482860e-01, (float)5.071479080e-01,  (float)5.508543250e-01,
+      (float)5.913437600e-01,  (float)4.606473150e-01,  (float)6.235318180e-01,
+      (float)8.454865210e-01,  (float)-2.929555180e-01, (float)-7.761295430e-01,
+      (float)1.069886090e-01,  (float)-5.386613610e-01, (float)1.911667440e-01,
+      (float)7.969756420e-02,  (float)9.220249200e-02,  (float)-5.815454130e-01,
+      (float)4.016868770e-02,  (float)2.783014180e-01,  (float)1.644356550e-01,
+      (float)-5.277292730e-01, (float)1.197944950e-01,  (float)9.721879660e-02,
+      (float)-4.874985520e-01, (float)-5.873894100e-01, (float)-5.865266320e-01,
+      (float)-3.519758280e-01, (float)-4.026320280e-01, (float)-8.600480560e-01,
+      (float)1.937968730e-01};
+  llvm::emitc::Tensor<float, 2> v82 = {(float)-4.473917190e-01, (float)-6.821969150e-02};
+  llvm::emitc::Tensor<int64_t, 999> v83 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v73);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v84 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v38);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v85 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v83);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v86 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v84, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v87 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v86, v85);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v88 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v87);
+  llvm::emitc::Tensor<int32_t, 1, 1> v89 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v88, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v90 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v89);
+  llvm::emitc::Tensor<int32_t, 1, 1> v91 = emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v90);
+  llvm::emitc::Tensor<float, 1, 1> v92 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v91);
+  llvm::emitc::Tensor<float, 1, 1> v93 = emitc::tosa::mul(v92, v40);
+  llvm::emitc::Tensor<float, 1, 1> v94 = emitc::tosa::mul(v93, v93);
+  llvm::emitc::Tensor<float, 1, 2> v95 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v93, v94);
+  llvm::emitc::Tensor<int64_t, 999> v96 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v72);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v97 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v1);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v98 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v96);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v99 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v97, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v100 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v99, v98);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v101 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v100);
+  llvm::emitc::Tensor<int32_t, 1, 1> v102 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v101, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v103 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v102);
+  llvm::emitc::Tensor<int32_t, 1, 1> v104 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v103);
+  llvm::emitc::Tensor<float, 1, 1> v105 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v104);
+  llvm::emitc::Tensor<float, 1, 1> v106 = emitc::tosa::mul(v105, v40);
+  llvm::emitc::Tensor<float, 1, 1> v107 = emitc::tosa::mul(v106, v106);
+  llvm::emitc::Tensor<float, 1, 2> v108 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v106, v107);
+  llvm::emitc::Tensor<int64_t, 999> v109 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v71);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v110 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v34);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v111 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v109);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v112 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v110, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v113 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v112, v111);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v114 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v113);
+  llvm::emitc::Tensor<int32_t, 1, 1> v115 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v114, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v116 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v115);
+  llvm::emitc::Tensor<int32_t, 1, 1> v117 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v116);
+  llvm::emitc::Tensor<float, 1, 1> v118 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v117);
+  llvm::emitc::Tensor<float, 1, 1> v119 = emitc::tosa::mul(v118, v40);
+  llvm::emitc::Tensor<float, 1, 1> v120 = emitc::tosa::mul(v119, v119);
+  llvm::emitc::Tensor<float, 1, 2> v121 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v119, v120);
+  llvm::emitc::Tensor<int64_t, 999> v122 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v70);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v123 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v17);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v124 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v122);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v125 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v123, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v126 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v125, v124);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v127 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v126);
+  llvm::emitc::Tensor<int32_t, 1, 1> v128 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v127, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v129 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v128);
+  llvm::emitc::Tensor<int32_t, 1, 1> v130 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v129);
+  llvm::emitc::Tensor<float, 1, 1> v131 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v130);
+  llvm::emitc::Tensor<float, 1, 1> v132 = emitc::tosa::mul(v131, v40);
+  llvm::emitc::Tensor<float, 1, 1> v133 = emitc::tosa::mul(v132, v132);
+  llvm::emitc::Tensor<float, 1, 2> v134 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v132, v133);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v135 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v5);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v136 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v135, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v137 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v136, v41);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v138 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v137);
+  llvm::emitc::Tensor<int32_t, 1, 1> v139 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v138, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v140 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v139);
+  llvm::emitc::Tensor<int32_t, 1, 1> v141 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v140);
+  llvm::emitc::Tensor<float, 1, 1> v142 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v141);
+  llvm::emitc::Tensor<float, 1, 1> v143 = emitc::tosa::mul(v142, v40);
+  llvm::emitc::Tensor<float, 1, 1> v144 = emitc::tosa::mul(v143, v143);
+  llvm::emitc::Tensor<float, 1, 2> v145 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v143, v144);
+  llvm::emitc::Tensor<int64_t, 999> v146 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v57);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v147 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v24);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v148 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v146);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v149 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v147, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v150 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v149, v148);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v151 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v150);
+  llvm::emitc::Tensor<int32_t, 1, 1> v152 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v151, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v153 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v152);
+  llvm::emitc::Tensor<int32_t, 1, 1> v154 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v153);
+  llvm::emitc::Tensor<float, 1, 1> v155 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v154);
+  llvm::emitc::Tensor<float, 1, 1> v156 = emitc::tosa::mul(v155, v40);
+  llvm::emitc::Tensor<float, 1, 1> v157 = emitc::tosa::mul(v156, v156);
+  llvm::emitc::Tensor<float, 1, 2> v158 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v156, v157);
+  llvm::emitc::Tensor<int64_t, 999> v159 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v69);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v160 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v35);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v161 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v159);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v162 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v160, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v163 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v162, v161);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v164 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v163);
+  llvm::emitc::Tensor<int32_t, 1, 1> v165 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v164, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v166 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v165);
+  llvm::emitc::Tensor<int32_t, 1, 1> v167 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v166);
+  llvm::emitc::Tensor<float, 1, 1> v168 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v167);
+  llvm::emitc::Tensor<float, 1, 1> v169 = emitc::tosa::mul(v168, v40);
+  llvm::emitc::Tensor<float, 1, 1> v170 = emitc::tosa::mul(v169, v169);
+  llvm::emitc::Tensor<float, 1, 2> v171 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v169, v170);
+  llvm::emitc::Tensor<int64_t, 999> v172 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v68);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v173 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v16);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v174 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v172);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v175 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v173, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v176 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v175, v174);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v177 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v176);
+  llvm::emitc::Tensor<int32_t, 1, 1> v178 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v177, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v179 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v178);
+  llvm::emitc::Tensor<int32_t, 1, 1> v180 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v179);
+  llvm::emitc::Tensor<float, 1, 1> v181 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v180);
+  llvm::emitc::Tensor<float, 1, 1> v182 = emitc::tosa::mul(v181, v40);
+  llvm::emitc::Tensor<float, 1, 1> v183 = emitc::tosa::mul(v182, v182);
+  llvm::emitc::Tensor<float, 1, 2> v184 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v182, v183);
+  llvm::emitc::Tensor<int64_t, 999> v185 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v67);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v186 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v23);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v187 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v185);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v188 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v186, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v189 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v188, v187);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v190 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v189);
+  llvm::emitc::Tensor<int32_t, 1, 1> v191 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v190, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v192 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v191);
+  llvm::emitc::Tensor<int32_t, 1, 1> v193 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v192);
+  llvm::emitc::Tensor<float, 1, 1> v194 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v193);
+  llvm::emitc::Tensor<float, 1, 1> v195 = emitc::tosa::mul(v194, v40);
+  llvm::emitc::Tensor<float, 1, 1> v196 = emitc::tosa::mul(v195, v195);
+  llvm::emitc::Tensor<float, 1, 2> v197 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v195, v196);
+  llvm::emitc::Tensor<int64_t, 999> v198 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v66);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v199 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v29);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v200 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v198);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v201 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v199, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v202 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v201, v200);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v203 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v202);
+  llvm::emitc::Tensor<int32_t, 1, 1> v204 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v203, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v205 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v204);
+  llvm::emitc::Tensor<int32_t, 1, 1> v206 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v205);
+  llvm::emitc::Tensor<float, 1, 1> v207 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v206);
+  llvm::emitc::Tensor<float, 1, 1> v208 = emitc::tosa::mul(v207, v40);
+  llvm::emitc::Tensor<float, 1, 1> v209 = emitc::tosa::mul(v208, v208);
+  llvm::emitc::Tensor<float, 1, 2> v210 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v208, v209);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v211 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v13);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v212 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v211, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v213 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v212, v41);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v214 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v213);
+  llvm::emitc::Tensor<int32_t, 1, 1> v215 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v214, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v216 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v215);
+  llvm::emitc::Tensor<int32_t, 1, 1> v217 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v216);
+  llvm::emitc::Tensor<float, 1, 1> v218 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v217);
+  llvm::emitc::Tensor<float, 1, 1> v219 = emitc::tosa::mul(v218, v40);
+  llvm::emitc::Tensor<float, 1, 1> v220 = emitc::tosa::mul(v219, v219);
+  llvm::emitc::Tensor<float, 1, 2> v221 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v219, v220);
+  llvm::emitc::Tensor<int64_t, 999> v222 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v65);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v223 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v2);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v224 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v222);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v225 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v223, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v226 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v225, v224);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v227 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v226);
+  llvm::emitc::Tensor<int32_t, 1, 1> v228 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v227, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v229 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v228);
+  llvm::emitc::Tensor<int32_t, 1, 1> v230 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v229);
+  llvm::emitc::Tensor<float, 1, 1> v231 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v230);
+  llvm::emitc::Tensor<float, 1, 1> v232 = emitc::tosa::mul(v231, v40);
+  llvm::emitc::Tensor<float, 1, 1> v233 = emitc::tosa::mul(v232, v232);
+  llvm::emitc::Tensor<float, 1, 2> v234 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v232, v233);
+  llvm::emitc::Tensor<int64_t, 999> v235 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v64);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v236 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v11);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v237 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v235);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v238 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v236, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v239 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v238, v237);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v240 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v239);
+  llvm::emitc::Tensor<int32_t, 1, 1> v241 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v240, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v242 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v241);
+  llvm::emitc::Tensor<int32_t, 1, 1> v243 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v242);
+  llvm::emitc::Tensor<float, 1, 1> v244 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v243);
+  llvm::emitc::Tensor<float, 1, 1> v245 = emitc::tosa::mul(v244, v40);
+  llvm::emitc::Tensor<float, 1, 1> v246 = emitc::tosa::mul(v245, v245);
+  llvm::emitc::Tensor<float, 1, 2> v247 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v245, v246);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v248 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v32);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v249 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v248, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v250 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v249, v41);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v251 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v250);
+  llvm::emitc::Tensor<int32_t, 1, 1> v252 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v251, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v253 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v252);
+  llvm::emitc::Tensor<int32_t, 1, 1> v254 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v253);
+  llvm::emitc::Tensor<float, 1, 1> v255 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v254);
+  llvm::emitc::Tensor<float, 1, 1> v256 = emitc::tosa::mul(v255, v40);
+  llvm::emitc::Tensor<float, 1, 1> v257 = emitc::tosa::mul(v256, v256);
+  llvm::emitc::Tensor<float, 1, 2> v258 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v256, v257);
+  llvm::emitc::Tensor<int64_t, 999> v259 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v63);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v260 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v14);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v261 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v259);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v262 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v260, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v263 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v262, v261);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v264 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v263);
+  llvm::emitc::Tensor<int32_t, 1, 1> v265 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v264, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v266 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v265);
+  llvm::emitc::Tensor<int32_t, 1, 1> v267 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v266);
+  llvm::emitc::Tensor<float, 1, 1> v268 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v267);
+  llvm::emitc::Tensor<float, 1, 1> v269 = emitc::tosa::mul(v268, v40);
+  llvm::emitc::Tensor<float, 1, 1> v270 = emitc::tosa::mul(v269, v269);
+  llvm::emitc::Tensor<float, 1, 2> v271 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v269, v270);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v272 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v10);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v273 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v272, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v274 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v273, v41);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v275 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v274);
+  llvm::emitc::Tensor<int32_t, 1, 1> v276 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v275, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v277 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v276);
+  llvm::emitc::Tensor<int32_t, 1, 1> v278 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v277);
+  llvm::emitc::Tensor<float, 1, 1> v279 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v278);
+  llvm::emitc::Tensor<float, 1, 1> v280 = emitc::tosa::mul(v279, v40);
+  llvm::emitc::Tensor<float, 1, 1> v281 = emitc::tosa::mul(v280, v280);
+  llvm::emitc::Tensor<float, 1, 2> v282 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v280, v281);
+  llvm::emitc::Tensor<int64_t, 999> v283 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v62);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v284 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v21);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v285 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v283);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v286 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v284, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v287 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v286, v285);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v288 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v287);
+  llvm::emitc::Tensor<int32_t, 1, 1> v289 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v288, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v290 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v289);
+  llvm::emitc::Tensor<int32_t, 1, 1> v291 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v290);
+  llvm::emitc::Tensor<float, 1, 1> v292 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v291);
+  llvm::emitc::Tensor<float, 1, 1> v293 = emitc::tosa::mul(v292, v40);
+  llvm::emitc::Tensor<float, 1, 1> v294 = emitc::tosa::mul(v293, v293);
+  llvm::emitc::Tensor<float, 1, 2> v295 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v293, v294);
+  llvm::emitc::Tensor<int64_t, 9999> v296 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 9999>>(v61);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v297 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v33);
+  llvm::emitc::Tensor<int64_t, 1, 1, 9999> v298 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 9999>>(v296);
+  llvm::emitc::Tensor<int64_t, 1, 1, 9999> v299 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 9999>>(v297, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 9999> v300 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 9999>>(v299, v298);
+  llvm::emitc::Tensor<int32_t, 1, 1, 9999> v301 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 9999>>(v300);
+  llvm::emitc::Tensor<int32_t, 1, 1> v302 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 9999>>(v301, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v303 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v302);
+  llvm::emitc::Tensor<int32_t, 1, 1> v304 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v303);
+  llvm::emitc::Tensor<float, 1, 1> v305 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v304);
+  llvm::emitc::Tensor<float, 1, 1> v306 = emitc::tosa::mul(v305, v42);
+  llvm::emitc::Tensor<float, 1, 1> v307 = emitc::tosa::mul(v306, v306);
+  llvm::emitc::Tensor<float, 1, 2> v308 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v306, v307);
+  llvm::emitc::Tensor<int64_t, 9999> v309 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 9999>>(v60);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v310 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v30);
+  llvm::emitc::Tensor<int64_t, 1, 1, 9999> v311 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 9999>>(v309);
+  llvm::emitc::Tensor<int64_t, 1, 1, 9999> v312 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 9999>>(v310, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 9999> v313 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 9999>>(v312, v311);
+  llvm::emitc::Tensor<int32_t, 1, 1, 9999> v314 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 9999>>(v313);
+  llvm::emitc::Tensor<int32_t, 1, 1> v315 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 9999>>(v314, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v316 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v315);
+  llvm::emitc::Tensor<int32_t, 1, 1> v317 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v316);
+  llvm::emitc::Tensor<float, 1, 1> v318 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v317);
+  llvm::emitc::Tensor<float, 1, 1> v319 = emitc::tosa::mul(v318, v42);
+  llvm::emitc::Tensor<float, 1, 1> v320 = emitc::tosa::mul(v319, v319);
+  llvm::emitc::Tensor<float, 1, 2> v321 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v319, v320);
+  llvm::emitc::Tensor<int64_t, 999> v322 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v59);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v323 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v19);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v324 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v322);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v325 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v323, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v326 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v325, v324);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v327 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v326);
+  llvm::emitc::Tensor<int32_t, 1, 1> v328 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v327, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v329 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v328);
+  llvm::emitc::Tensor<int32_t, 1, 1> v330 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v329);
+  llvm::emitc::Tensor<float, 1, 1> v331 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v330);
+  llvm::emitc::Tensor<float, 1, 1> v332 = emitc::tosa::mul(v331, v40);
+  llvm::emitc::Tensor<float, 1, 1> v333 = emitc::tosa::mul(v332, v332);
+  llvm::emitc::Tensor<float, 1, 2> v334 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v332, v333);
+  llvm::emitc::Tensor<int64_t, 999> v335 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v58);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v336 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v18);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v337 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v335);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v338 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v336, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v339 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v338, v337);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v340 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v339);
+  llvm::emitc::Tensor<int32_t, 1, 1> v341 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v340, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v342 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v341);
+  llvm::emitc::Tensor<int32_t, 1, 1> v343 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v342);
+  llvm::emitc::Tensor<float, 1, 1> v344 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v343);
+  llvm::emitc::Tensor<float, 1, 1> v345 = emitc::tosa::mul(v344, v40);
+  llvm::emitc::Tensor<float, 1, 1> v346 = emitc::tosa::mul(v345, v345);
+  llvm::emitc::Tensor<float, 1, 2> v347 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v345, v346);
+  llvm::emitc::Tensor<int64_t, 999> v348 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v57);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v349 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v9);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v350 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v348);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v351 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v349, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v352 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v351, v350);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v353 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v352);
+  llvm::emitc::Tensor<int32_t, 1, 1> v354 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v353, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v355 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v354);
+  llvm::emitc::Tensor<int32_t, 1, 1> v356 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v355);
+  llvm::emitc::Tensor<float, 1, 1> v357 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v356);
+  llvm::emitc::Tensor<float, 1, 1> v358 = emitc::tosa::mul(v357, v40);
+  llvm::emitc::Tensor<float, 1, 1> v359 = emitc::tosa::mul(v358, v358);
+  llvm::emitc::Tensor<float, 1, 2> v360 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v358, v359);
+  llvm::emitc::Tensor<int64_t, 999> v361 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v56);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v362 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v27);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v363 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v361);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v364 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v362, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v365 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v364, v363);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v366 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v365);
+  llvm::emitc::Tensor<int32_t, 1, 1> v367 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v366, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v368 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v367);
+  llvm::emitc::Tensor<int32_t, 1, 1> v369 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v368);
+  llvm::emitc::Tensor<float, 1, 1> v370 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v369);
+  llvm::emitc::Tensor<float, 1, 1> v371 = emitc::tosa::mul(v370, v40);
+  llvm::emitc::Tensor<float, 1, 1> v372 = emitc::tosa::mul(v371, v371);
+  llvm::emitc::Tensor<float, 1, 2> v373 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v371, v372);
+  llvm::emitc::Tensor<int64_t, 999> v374 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v55);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v375 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v26);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v376 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v374);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v377 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v375, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v378 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v377, v376);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v379 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v378);
+  llvm::emitc::Tensor<int32_t, 1, 1> v380 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v379, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v381 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v380);
+  llvm::emitc::Tensor<int32_t, 1, 1> v382 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v381);
+  llvm::emitc::Tensor<float, 1, 1> v383 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v382);
+  llvm::emitc::Tensor<float, 1, 1> v384 = emitc::tosa::mul(v383, v40);
+  llvm::emitc::Tensor<float, 1, 1> v385 = emitc::tosa::mul(v384, v384);
+  llvm::emitc::Tensor<float, 1, 2> v386 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v384, v385);
+  llvm::emitc::Tensor<int64_t, 999> v387 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v54);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v388 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v25);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v389 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v387);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v390 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v388, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v391 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v390, v389);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v392 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v391);
+  llvm::emitc::Tensor<int32_t, 1, 1> v393 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v392, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v394 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v393);
+  llvm::emitc::Tensor<int32_t, 1, 1> v395 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v394);
+  llvm::emitc::Tensor<float, 1, 1> v396 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v395);
+  llvm::emitc::Tensor<float, 1, 1> v397 = emitc::tosa::mul(v396, v40);
+  llvm::emitc::Tensor<float, 1, 1> v398 = emitc::tosa::mul(v397, v397);
+  llvm::emitc::Tensor<float, 1, 2> v399 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v397, v398);
+  llvm::emitc::Tensor<int64_t, 999> v400 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v53);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v401 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v20);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v402 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v400);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v403 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v401, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v404 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v403, v402);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v405 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v404);
+  llvm::emitc::Tensor<int32_t, 1, 1> v406 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v405, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v407 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v406);
+  llvm::emitc::Tensor<int32_t, 1, 1> v408 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v407);
+  llvm::emitc::Tensor<float, 1, 1> v409 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v408);
+  llvm::emitc::Tensor<float, 1, 1> v410 = emitc::tosa::mul(v409, v40);
+  llvm::emitc::Tensor<float, 1, 1> v411 = emitc::tosa::mul(v410, v410);
+  llvm::emitc::Tensor<float, 1, 2> v412 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v410, v411);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v413 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v36);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v414 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v413, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v415 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v414, v41);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v416 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v415);
+  llvm::emitc::Tensor<int32_t, 1, 1> v417 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v416, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v418 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v417);
+  llvm::emitc::Tensor<int32_t, 1, 1> v419 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v418);
+  llvm::emitc::Tensor<float, 1, 1> v420 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v419);
+  llvm::emitc::Tensor<float, 1, 1> v421 = emitc::tosa::mul(v420, v40);
+  llvm::emitc::Tensor<float, 1, 1> v422 = emitc::tosa::mul(v421, v421);
+  llvm::emitc::Tensor<float, 1, 2> v423 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v421, v422);
+  llvm::emitc::Tensor<int64_t, 999> v424 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v52);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v425 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v22);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v426 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v424);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v427 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v425, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v428 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v427, v426);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v429 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v428);
+  llvm::emitc::Tensor<int32_t, 1, 1> v430 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v429, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v431 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v430);
+  llvm::emitc::Tensor<int32_t, 1, 1> v432 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v431);
+  llvm::emitc::Tensor<float, 1, 1> v433 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v432);
+  llvm::emitc::Tensor<float, 1, 1> v434 = emitc::tosa::mul(v433, v40);
+  llvm::emitc::Tensor<float, 1, 1> v435 = emitc::tosa::mul(v434, v434);
+  llvm::emitc::Tensor<float, 1, 2> v436 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v434, v435);
+  llvm::emitc::Tensor<int64_t, 999> v437 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v51);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v438 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v12);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v439 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v437);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v440 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v438, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v441 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v440, v439);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v442 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v441);
+  llvm::emitc::Tensor<int32_t, 1, 1> v443 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v442, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v444 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v443);
+  llvm::emitc::Tensor<int32_t, 1, 1> v445 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v444);
+  llvm::emitc::Tensor<float, 1, 1> v446 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v445);
+  llvm::emitc::Tensor<float, 1, 1> v447 = emitc::tosa::mul(v446, v40);
+  llvm::emitc::Tensor<float, 1, 1> v448 = emitc::tosa::mul(v447, v447);
+  llvm::emitc::Tensor<float, 1, 2> v449 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v447, v448);
+  llvm::emitc::Tensor<int64_t, 999> v450 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v50);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v451 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v15);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v452 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v450);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v453 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v451, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v454 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v453, v452);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v455 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v454);
+  llvm::emitc::Tensor<int32_t, 1, 1> v456 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v455, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v457 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v456);
+  llvm::emitc::Tensor<int32_t, 1, 1> v458 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v457);
+  llvm::emitc::Tensor<float, 1, 1> v459 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v458);
+  llvm::emitc::Tensor<float, 1, 1> v460 = emitc::tosa::mul(v459, v40);
+  llvm::emitc::Tensor<float, 1, 1> v461 = emitc::tosa::mul(v460, v460);
+  llvm::emitc::Tensor<float, 1, 2> v462 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v460, v461);
+  llvm::emitc::Tensor<int64_t, 999> v463 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v49);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v464 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v8);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v465 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v463);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v466 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v464, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v467 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v466, v465);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v468 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v467);
+  llvm::emitc::Tensor<int32_t, 1, 1> v469 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v468, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v470 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v469);
+  llvm::emitc::Tensor<int32_t, 1, 1> v471 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v470);
+  llvm::emitc::Tensor<float, 1, 1> v472 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v471);
+  llvm::emitc::Tensor<float, 1, 1> v473 = emitc::tosa::mul(v472, v40);
+  llvm::emitc::Tensor<float, 1, 1> v474 = emitc::tosa::mul(v473, v473);
+  llvm::emitc::Tensor<float, 1, 2> v475 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v473, v474);
+  llvm::emitc::Tensor<int64_t, 999> v476 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v48);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v477 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v6);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v478 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v476);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v479 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v477, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v480 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v479, v478);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v481 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v480);
+  llvm::emitc::Tensor<int32_t, 1, 1> v482 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v481, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v483 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v482);
+  llvm::emitc::Tensor<int32_t, 1, 1> v484 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v483);
+  llvm::emitc::Tensor<float, 1, 1> v485 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v484);
+  llvm::emitc::Tensor<float, 1, 1> v486 = emitc::tosa::mul(v485, v40);
+  llvm::emitc::Tensor<float, 1, 1> v487 = emitc::tosa::mul(v486, v486);
+  llvm::emitc::Tensor<float, 1, 2> v488 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v486, v487);
+  llvm::emitc::Tensor<int64_t, 999> v489 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v47);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v490 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v7);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v491 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v489);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v492 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v490, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v493 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v492, v491);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v494 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v493);
+  llvm::emitc::Tensor<int32_t, 1, 1> v495 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v494, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v496 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v495);
+  llvm::emitc::Tensor<int32_t, 1, 1> v497 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v496);
+  llvm::emitc::Tensor<float, 1, 1> v498 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v497);
+  llvm::emitc::Tensor<float, 1, 1> v499 = emitc::tosa::mul(v498, v40);
+  llvm::emitc::Tensor<float, 1, 1> v500 = emitc::tosa::mul(v499, v499);
+  llvm::emitc::Tensor<float, 1, 2> v501 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v499, v500);
+  llvm::emitc::Tensor<int64_t, 999> v502 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v46);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v503 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v31);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v504 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v502);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v505 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v503, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v506 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v505, v504);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v507 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v506);
+  llvm::emitc::Tensor<int32_t, 1, 1> v508 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v507, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v509 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v508);
+  llvm::emitc::Tensor<int32_t, 1, 1> v510 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v509);
+  llvm::emitc::Tensor<float, 1, 1> v511 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v510);
+  llvm::emitc::Tensor<float, 1, 1> v512 = emitc::tosa::mul(v511, v40);
+  llvm::emitc::Tensor<float, 1, 1> v513 = emitc::tosa::mul(v512, v512);
+  llvm::emitc::Tensor<float, 1, 2> v514 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v512, v513);
+  llvm::emitc::Tensor<int64_t, 999> v515 = emitc::tosa::cast<llvm::emitc::Tensor<int64_t, 999>>(v45);
+  llvm::emitc::Tensor<int64_t, 1, 1, 1> v516 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 1>>(v3);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v517 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v515);
+  llvm::emitc::Tensor<int64_t, 1, 1, 999> v518 =
+      emitc::broadcast_in_dim<llvm::emitc::Tensor<int64_t, 1, 1, 999>>(v516, {0, 1, 2});
+  llvm::emitc::Tensor<bool, 1, 1, 999> v519 =
+      emitc::tosa::greater_equal<llvm::emitc::Tensor<bool, 1, 1, 999>>(v518, v517);
+  llvm::emitc::Tensor<int32_t, 1, 1, 999> v520 =
+      emitc::tosa::cast<llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v519);
+  llvm::emitc::Tensor<int32_t, 1, 1> v521 =
+      emitc::tosa::reduce_sum<llvm::emitc::Tensor<int32_t, 1, 1>,
+                              llvm::emitc::Tensor<int32_t, 1, 1, 999>>(v520, 2);
+  llvm::emitc::Tensor<int32_t, 1, 1, 1> v522 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1, 1>>(v521);
+  llvm::emitc::Tensor<int32_t, 1, 1> v523 =
+      emitc::tosa::reshape<llvm::emitc::Tensor<int32_t, 1, 1>>(v522);
+  llvm::emitc::Tensor<float, 1, 1> v524 = emitc::tosa::cast<llvm::emitc::Tensor<float, 1, 1>>(v523);
+  llvm::emitc::Tensor<float, 1, 1> v525 = emitc::tosa::mul(v524, v40);
+  llvm::emitc::Tensor<float, 1, 1> v526 = emitc::tosa::mul(v525, v525);
+  llvm::emitc::Tensor<float, 1, 2> v527 =
+      emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 2>>(v525, v526);
+  llvm::emitc::Tensor<float, 1, 71> v528 = emitc::tosa::concat<1, llvm::emitc::Tensor<float, 1, 71>>(
+      v334, v462, v475, v488, v501, v514, v527, v95, v108, v121, v134, v145,
+      v158, v171, v184, v197, v210, v221, v74, v234, v247, v258, v271, v282,
+      v295, v308, v321, v347, v360, v373, v386, v399, v412, v423, v436, v449);
+  llvm::emitc::Tensor<float, 1, 40> v529 =
+      emitc::tosa::fully_connected<llvm::emitc::Tensor<float, 1, 40>>(v528, v75, v76);
+  llvm::emitc::Tensor<float, 1, 40> v530 =
+      emitc::tosa::clamp(v529, (float)0.0e+00, (float)3.402823470e+38);
+  llvm::emitc::Tensor<float, 1, 40> v531 =
+      emitc::tosa::fully_connected<llvm::emitc::Tensor<float, 1, 40>>(v530, v77, v78);
+  llvm::emitc::Tensor<float, 1, 40> v532 =
+      emitc::tosa::clamp(v531, (float)0.0e+00, (float)3.402823470e+38);
+  llvm::emitc::Tensor<float, 1, 20> v533 =
+      emitc::tosa::fully_connected<llvm::emitc::Tensor<float, 1, 20>>(v532, v79, v80);
+  llvm::emitc::Tensor<float, 1, 20> v534 =
+      emitc::tosa::clamp(v533, (float)0.0e+00, (float)3.402823470e+38);
+  llvm::emitc::Tensor<float, 1, 2> v535 =
+      emitc::tosa::fully_connected<llvm::emitc::Tensor<float, 1, 2>>(v534, v81, v82);
+  llvm::emitc::Tensor<int64_t, 1> v536 =
+      emitc::tosa::argmax<llvm::emitc::Tensor<int64_t, 1>, llvm::emitc::Tensor<float, 1, 2>>(v535, 1);
+  llvm::emitc::Tensor<int64_t, 1> v537 = emitc::tosa::minimum(v536, v43);
+  llvm::emitc::Tensor<int64_t, 1> v538 = emitc::tosa::maximum(v537, v44);
+  return v538;
+}
+
+REGISTER_EMITC_MODEL(llvm::MLInlineOzEmitCModel, action);
+
+#include <iostream>
+class X {
+  public:
+    X() { std::cout << "\n\nasdf\n\n" << std::endl; }
+};
+
+namespace {
+  X x;
+}
+
+int foo() {return 0;}
diff --git a/llvm/lib/Analysis/models/emitc/PluginInfo.cpp b/llvm/lib/Analysis/models/emitc/PluginInfo.cpp
new file mode 100644
--- /dev/null
+++ b/llvm/lib/Analysis/models/emitc/PluginInfo.cpp
@@ -0,0 +1,20 @@
+#include "MLInlinerSizeModel.h"
+#include "llvm/Passes/PassPlugin.h"
+
+namespace {
+uint32_t APIVersion = 0;
+const char *PluginName = "EmitCModels";
+const char *PluginVersion = "0.0";
+} // namespace
+
+void RegisterPassBuilderCallback(llvm::PassBuilder &PB) {
+  // Do nothing...
+}
+
+llvm::PassPluginLibraryInfo getEmitCModelsPluginInfo() {
+  return llvm::PassPluginLibraryInfo{.APIVersion = 0,
+                                     .PluginName = "EmitCModels",
+                                     .PluginVersion = "0.0",
+                                     .RegisterPassBuilderCallbacks =
+                                         &RegisterPassBuilderCallback};
+}
diff --git a/llvm/lib/Analysis/models/gen-inline-oz-test-model.py b/llvm/lib/Analysis/models/gen-inline-oz-test-model.py
--- a/llvm/lib/Analysis/models/gen-inline-oz-test-model.py
+++ b/llvm/lib/Analysis/models/gen-inline-oz-test-model.py
@@ -21,7 +21,7 @@
             "port": 0,
             "type": "int64_t",
             "shape": [
-                1
+                1,
             ]
         }
     }
@@ -34,7 +34,8 @@
   """Returns the list of features for LLVM inlining."""
   # int64 features
   inputs = [
-      tf.TensorSpec(dtype=tf.int64, shape=(), name=key) for key in [
+      tf.TensorSpec(dtype=tf.int64, shape=(1,), name=key)
+      for key in [
           'caller_basic_block_count',
           'caller_conditionally_executed_blocks',
           'caller_users',
@@ -75,16 +76,20 @@
   ]
 
   # float32 features
-  inputs.extend([
-      tf.TensorSpec(dtype=tf.float32, shape=(), name=key)
-      for key in ['discount', 'reward']
-  ])
+  inputs.extend(
+      [
+          tf.TensorSpec(dtype=tf.float32, shape=(1,), name=key)
+          for key in ['discount', 'reward']
+      ]
+  )
 
   # int32 features
-  inputs.extend([
-      tf.TensorSpec(dtype=tf.int32, shape=(), name=key)
-      for key in ['step_type']
-  ])
+  inputs.extend(
+      [
+          tf.TensorSpec(dtype=tf.int32, shape=(1,), name=key)
+          for key in ['step_type']
+      ]
+  )
   return inputs
 
 
@@ -95,6 +100,7 @@
 def get_output_spec():
   return POLICY_OUTPUT_SPEC
 
+
 def get_output_spec_path(path):
   return os.path.join(path, 'output_spec.json')
 
@@ -102,8 +108,11 @@
 def build_mock_model(path, signature):
   """Build and save the mock model with the given signature"""
   module = tf.Module()
+
   def action(*inputs):
-    return {signature['output']: tf.constant(value=1, dtype=tf.int64)}
+    return {
+        signature['output']: tf.constant(value=1, shape=(1,), dtype=tf.int64)
+    }
 
   module.action = tf.function()(action)
   action = {'action': module.action.get_concrete_function(signature['inputs'])}
@@ -119,7 +128,7 @@
   return {
       'inputs': get_input_signature(),
       'output': get_output_signature(),
-      'output_spec': get_output_spec()
+      'output_spec': get_output_spec(),
   }
 
 
diff --git a/llvm/test/Transforms/Inline/ML/ml-test-emitc-mode.ll b/llvm/test/Transforms/Inline/ML/ml-test-emitc-mode.ll
--- a/llvm/test/Transforms/Inline/ML/ml-test-emitc-mode.ll
+++ b/llvm/test/Transforms/Inline/ML/ml-test-emitc-mode.ll
@@ -5,6 +5,5 @@
 ; This test uses Inputs/test-module.ll, as it will share it with a similar test
 ; for the 'development' and 'release' mode.
 ;
-; REQUIRES: llvm_inliner_model_emitc
-; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=emitc -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=CHECK
+; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=emitc -inliner-emitc-model-name=mymodel -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=CHECK
 ; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=default -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=DEFAULT
diff --git a/llvm/test/lit.cfg.py b/llvm/test/lit.cfg.py
--- a/llvm/test/lit.cfg.py
+++ b/llvm/test/lit.cfg.py
@@ -39,8 +39,7 @@
 llvm_config.with_environment('PATH', config.llvm_tools_dir, append_path=True)
 
 # Propagate some variables from the host environment.
-llvm_config.with_system_environment(
-    ['HOME', 'INCLUDE', 'LIB', 'TMP', 'TEMP'])
+llvm_config.with_system_environment(['HOME', 'INCLUDE', 'LIB', 'TMP', 'TEMP'])
 
 
 # Set up OCAMLPATH to include newly built OCaml libraries.
@@ -53,7 +52,8 @@
 
 llvm_config.with_system_environment('CAML_LD_LIBRARY_PATH')
 llvm_config.with_environment(
-    'CAML_LD_LIBRARY_PATH', llvm_ocaml_lib, append_path=True)
+    'CAML_LD_LIBRARY_PATH', llvm_ocaml_lib, append_path=True
+)
 
 # Set up OCAMLRUNPARAM to enable backtraces in OCaml tests.
 llvm_config.with_environment('OCAMLRUNPARAM', 'b')
@@ -63,24 +63,27 @@
 
 
 def get_asan_rtlib():
-    if not 'Address' in config.llvm_use_sanitizer or \
-       not 'Darwin' in config.host_os or \
-       not 'x86' in config.host_triple:
-        return ''
-    try:
-        import glob
-    except:
-        print('glob module not found, skipping get_asan_rtlib() lookup')
-        return ''
-    # The libclang_rt.asan_osx_dynamic.dylib path is obtained using the relative
-    # path from the host cc.
-    host_lib_dir = os.path.join(os.path.dirname(config.host_cc), '../lib')
-    asan_dylib_dir_pattern = host_lib_dir + \
-        '/clang/*/lib/darwin/libclang_rt.asan_osx_dynamic.dylib'
-    found_dylibs = glob.glob(asan_dylib_dir_pattern)
-    if len(found_dylibs) != 1:
-        return ''
-    return found_dylibs[0]
+  if (
+      not 'Address' in config.llvm_use_sanitizer
+      or not 'Darwin' in config.host_os
+      or not 'x86' in config.host_triple
+  ):
+    return ''
+  try:
+    import glob
+  except:
+    print('glob module not found, skipping get_asan_rtlib() lookup')
+    return ''
+  # The libclang_rt.asan_osx_dynamic.dylib path is obtained using the relative
+  # path from the host cc.
+  host_lib_dir = os.path.join(os.path.dirname(config.host_cc), '../lib')
+  asan_dylib_dir_pattern = (
+      host_lib_dir + '/clang/*/lib/darwin/libclang_rt.asan_osx_dynamic.dylib'
+  )
+  found_dylibs = glob.glob(asan_dylib_dir_pattern)
+  if len(found_dylibs) != 1:
+    return ''
+  return found_dylibs[0]
 
 
 llvm_config.use_default_substitutions()
@@ -99,14 +102,13 @@
 # Windows.  FIXME: the process target triple should be used here, but this is
 # difficult to obtain on Windows.
 if re.search(r'cygwin|windows-gnu|windows-msvc', config.host_triple):
-    lli_args = ['-mtriple=' + config.host_triple + '-elf']
+  lli_args = ['-mtriple=' + config.host_triple + '-elf']
 
 llc_args = []
 
 # Similarly, have a macro to use llc with DWARF even when the host is Windows
 if re.search(r'windows-msvc', config.target_triple):
-    llc_args = [' -mtriple=' +
-                config.target_triple.replace('-msvc', '-gnu')]
+  llc_args = [' -mtriple=' + config.target_triple.replace('-msvc', '-gnu')]
 
 # Provide the path to asan runtime lib if available. On darwin, this lib needs
 # to be loaded via DYLD_INSERT_LIBRARIES before libLTO.dylib in case the files
@@ -114,28 +116,42 @@
 ld64_cmd = config.ld64_executable
 asan_rtlib = get_asan_rtlib()
 if asan_rtlib:
-    ld64_cmd = 'DYLD_INSERT_LIBRARIES={} {}'.format(asan_rtlib, ld64_cmd)
+  ld64_cmd = 'DYLD_INSERT_LIBRARIES={} {}'.format(asan_rtlib, ld64_cmd)
 if config.osx_sysroot:
-    ld64_cmd = '{} -syslibroot {}'.format(ld64_cmd, config.osx_sysroot)
+  ld64_cmd = '{} -syslibroot {}'.format(ld64_cmd, config.osx_sysroot)
 
 ocamlc_command = '%s ocamlc -cclib -L%s %s' % (
-    config.ocamlfind_executable, config.llvm_lib_dir, config.ocaml_flags)
+    config.ocamlfind_executable,
+    config.llvm_lib_dir,
+    config.ocaml_flags,
+)
 ocamlopt_command = 'true'
 if config.have_ocamlopt:
-    ocamlopt_command = '%s ocamlopt -cclib -L%s -cclib -Wl,-rpath,%s %s' % (
-        config.ocamlfind_executable, config.llvm_lib_dir, config.llvm_lib_dir, config.ocaml_flags)
-
-opt_viewer_cmd = '%s %s/tools/opt-viewer/opt-viewer.py' % (sys.executable, config.llvm_src_root)
+  ocamlopt_command = '%s ocamlopt -cclib -L%s -cclib -Wl,-rpath,%s %s' % (
+      config.ocamlfind_executable,
+      config.llvm_lib_dir,
+      config.llvm_lib_dir,
+      config.ocaml_flags,
+  )
+
+opt_viewer_cmd = '%s %s/tools/opt-viewer/opt-viewer.py' % (
+    sys.executable,
+    config.llvm_src_root,
+)
 
 llvm_original_di_preservation_cmd = os.path.join(
-    config.llvm_src_root,'utils', 'llvm-original-di-preservation.py')
-config.substitutions.append(
-    ('%llvm-original-di-preservation', "'%s' %s" % (
-        config.python_executable, llvm_original_di_preservation_cmd)))
+    config.llvm_src_root, 'utils', 'llvm-original-di-preservation.py'
+)
+config.substitutions.append((
+    '%llvm-original-di-preservation',
+    "'%s' %s" % (config.python_executable, llvm_original_di_preservation_cmd),
+))
 
 llvm_locstats_tool = os.path.join(config.llvm_tools_dir, 'llvm-locstats')
-config.substitutions.append(
-    ('%llvm-locstats', "'%s' %s" % (config.python_executable, llvm_locstats_tool)))
+config.substitutions.append((
+    '%llvm-locstats',
+    "'%s' %s" % (config.python_executable, llvm_locstats_tool),
+))
 config.llvm_locstats_used = os.path.exists(llvm_locstats_tool)
 
 tools = [
@@ -156,22 +172,78 @@
 
 # FIXME: Why do we have both `lli` and `%lli` that do slightly different things?
 tools.extend([
-    'dsymutil', 'lli', 'lli-child-target', 'llvm-ar', 'llvm-as',
-    'llvm-addr2line', 'llvm-bcanalyzer', 'llvm-bitcode-strip', 'llvm-config',
-    'llvm-cov', 'llvm-cxxdump', 'llvm-cvtres', 'llvm-debuginfod-find', 'llvm-debuginfod',
+    'dsymutil',
+    'lli',
+    'lli-child-target',
+    'llvm-ar',
+    'llvm-as',
+    'llvm-addr2line',
+    'llvm-bcanalyzer',
+    'llvm-bitcode-strip',
+    'llvm-config',
+    'llvm-cov',
+    'llvm-cxxdump',
+    'llvm-cvtres',
+    'llvm-debuginfod-find',
+    'llvm-debuginfod',
     'llvm-debuginfo-analyzer',
-    'llvm-diff', 'llvm-dis', 'llvm-dwarfdump', 'llvm-dwarfutil', 'llvm-dlltool',
-    'llvm-exegesis', 'llvm-extract', 'llvm-isel-fuzzer', 'llvm-ifs',
-    'llvm-install-name-tool', 'llvm-jitlink', 'llvm-opt-fuzzer', 'llvm-lib',
-    'llvm-link', 'llvm-lto', 'llvm-lto2', 'llvm-mc', 'llvm-mca',
-    'llvm-modextract', 'llvm-nm', 'llvm-objcopy', 'llvm-objdump', 'llvm-otool',
-    'llvm-pdbutil', 'llvm-profdata', 'llvm-profgen', 'llvm-ranlib', 'llvm-rc', 'llvm-readelf',
-    'llvm-readobj', 'llvm-remark-size-diff', 'llvm-rtdyld', 'llvm-sim',
-    'llvm-size', 'llvm-split', 'llvm-stress', 'llvm-strings', 'llvm-strip',
-    'llvm-tblgen', 'llvm-tapi-diff', 'llvm-undname', 'llvm-windres',
-    'llvm-c-test', 'llvm-cxxfilt', 'llvm-xray', 'yaml2obj', 'obj2yaml',
-    'yaml-bench', 'verify-uselistorder', 'bugpoint', 'llc', 'llvm-symbolizer',
-    'opt', 'sancov', 'sanstats', 'llvm-remarkutil'])
+    'llvm-diff',
+    'llvm-dis',
+    'llvm-dwarfdump',
+    'llvm-dwarfutil',
+    'llvm-dlltool',
+    'llvm-exegesis',
+    'llvm-extract',
+    'llvm-isel-fuzzer',
+    'llvm-ifs',
+    'llvm-install-name-tool',
+    'llvm-jitlink',
+    'llvm-opt-fuzzer',
+    'llvm-lib',
+    'llvm-link',
+    'llvm-lto',
+    'llvm-lto2',
+    'llvm-mc',
+    'llvm-mca',
+    'llvm-modextract',
+    'llvm-nm',
+    'llvm-objcopy',
+    'llvm-objdump',
+    'llvm-otool',
+    'llvm-pdbutil',
+    'llvm-profdata',
+    'llvm-profgen',
+    'llvm-ranlib',
+    'llvm-rc',
+    'llvm-readelf',
+    'llvm-readobj',
+    'llvm-remark-size-diff',
+    'llvm-rtdyld',
+    'llvm-sim',
+    'llvm-size',
+    'llvm-split',
+    'llvm-stress',
+    'llvm-strings',
+    'llvm-strip',
+    'llvm-tblgen',
+    'llvm-tapi-diff',
+    'llvm-undname',
+    'llvm-windres',
+    'llvm-c-test',
+    'llvm-cxxfilt',
+    'llvm-xray',
+    'yaml2obj',
+    'obj2yaml',
+    'yaml-bench',
+    'verify-uselistorder',
+    'bugpoint',
+    'llc',
+    'llvm-symbolizer',
+    'opt',
+    'sancov',
+    'sanstats',
+    'llvm-remarkutil',
+])
 
 # The following tools are optional
 tools.extend([
@@ -190,62 +262,83 @@
     ToolSubst('OrcV2CBindingsReflectProcessSymbols', unresolved='ignore'),
     ToolSubst('OrcV2CBindingsLazy', unresolved='ignore'),
     ToolSubst('OrcV2CBindingsVeryLazy', unresolved='ignore'),
-    ToolSubst('dxil-dis', unresolved='ignore')])
+    ToolSubst('dxil-dis', unresolved='ignore'),
+])
+
 
 # Find (major, minor) version of ptxas
 def ptxas_version(ptxas):
-    ptxas_cmd = subprocess.Popen([ptxas, '--version'], stdout=subprocess.PIPE)
-    ptxas_out = ptxas_cmd.stdout.read().decode('ascii')
-    ptxas_cmd.wait()
-    match = re.search('release (\d+)\.(\d+)', ptxas_out)
-    if match:
-        return (int(match.group(1)), int(match.group(2)))
-    print('couldn\'t determine ptxas version')
-    return None
+  ptxas_cmd = subprocess.Popen([ptxas, '--version'], stdout=subprocess.PIPE)
+  ptxas_out = ptxas_cmd.stdout.read().decode('ascii')
+  ptxas_cmd.wait()
+  match = re.search('release (\d+)\.(\d+)', ptxas_out)
+  if match:
+    return (int(match.group(1)), int(match.group(2)))
+  print("couldn't determine ptxas version")
+  return None
+
 
 # Enable %ptxas and %ptxas-verify tools.
 # %ptxas-verify defaults to sm_60 architecture. It can be overriden
 # by specifying required one, for instance: %ptxas-verify -arch=sm_80.
 def enable_ptxas(ptxas_executable):
-    version = ptxas_version(ptxas_executable)
-    if version:
-        # ptxas is supposed to be backward compatible with previous
-        # versions, so add a feature for every known version prior to
-        # the current one.
-        ptxas_known_versions = [
-            (9, 0), (9, 1), (9, 2),
-            (10, 0), (10, 1), (10, 2),
-            (11, 0), (11, 1), (11, 2), (11, 3), (11, 4), (11, 5), (11, 6),
-            (11, 7), (11, 8), (12, 0),
-        ]
-
-        def version_int(ver):
-            return ver[0] * 100 + ver[1]
-
-        # ignore ptxas if its version is below the minimum supported
-        # version
-        min_version = ptxas_known_versions[0]
-        if version_int(version) < version_int(min_version):
-            print(
-                'Warning: ptxas version {}.{} is not supported'.format(
-                    version[0], version[1]))
-            return
-
-        for known_version in ptxas_known_versions:
-            if version_int(known_version) <= version_int(version):
-                major, minor = known_version
-                config.available_features.add(
-                    'ptxas-{}.{}'.format(major, minor))
-
-    config.available_features.add('ptxas')
-    tools.extend([ToolSubst('%ptxas', ptxas_executable),
-                  ToolSubst('%ptxas-verify', '{} -arch=sm_60 -c -'.format(
-                      ptxas_executable))])
-
-ptxas_executable = \
+  version = ptxas_version(ptxas_executable)
+  if version:
+    # ptxas is supposed to be backward compatible with previous
+    # versions, so add a feature for every known version prior to
+    # the current one.
+    ptxas_known_versions = [
+        (9, 0),
+        (9, 1),
+        (9, 2),
+        (10, 0),
+        (10, 1),
+        (10, 2),
+        (11, 0),
+        (11, 1),
+        (11, 2),
+        (11, 3),
+        (11, 4),
+        (11, 5),
+        (11, 6),
+        (11, 7),
+        (11, 8),
+        (12, 0),
+    ]
+
+    def version_int(ver):
+      return ver[0] * 100 + ver[1]
+
+    # ignore ptxas if its version is below the minimum supported
+    # version
+    min_version = ptxas_known_versions[0]
+    if version_int(version) < version_int(min_version):
+      print(
+          'Warning: ptxas version {}.{} is not supported'.format(
+              version[0], version[1]
+          )
+      )
+      return
+
+    for known_version in ptxas_known_versions:
+      if version_int(known_version) <= version_int(version):
+        major, minor = known_version
+        config.available_features.add('ptxas-{}.{}'.format(major, minor))
+
+  config.available_features.add('ptxas')
+  tools.extend([
+      ToolSubst('%ptxas', ptxas_executable),
+      ToolSubst(
+          '%ptxas-verify', '{} -arch=sm_60 -c -'.format(ptxas_executable)
+      ),
+  ])
+
+
+ptxas_executable = (
     os.environ.get('LLVM_PTXAS_EXECUTABLE', None) or config.ptxas_executable
+)
 if ptxas_executable:
-    enable_ptxas(ptxas_executable)
+  enable_ptxas(ptxas_executable)
 
 llvm_config.add_tool_substitutions(tools, config.llvm_tools_dir)
 
@@ -254,119 +347,128 @@
 config.targets = frozenset(config.targets_to_build.split())
 
 for arch in config.targets_to_build.split():
-    config.available_features.add(arch.lower() + '-registered-target')
+  config.available_features.add(arch.lower() + '-registered-target')
 
 # Features
-known_arches = ["x86_64", "mips64", "ppc64", "aarch64"]
-if (config.host_ldflags.find("-m32") < 0
-    and any(config.llvm_host_triple.startswith(x) for x in known_arches)):
-  config.available_features.add("llvm-64-bits")
+known_arches = ['x86_64', 'mips64', 'ppc64', 'aarch64']
+if config.host_ldflags.find('-m32') < 0 and any(
+    config.llvm_host_triple.startswith(x) for x in known_arches
+):
+  config.available_features.add('llvm-64-bits')
 
-config.available_features.add("host-byteorder-" + sys.byteorder + "-endian")
+config.available_features.add('host-byteorder-' + sys.byteorder + '-endian')
 
 if sys.platform in ['win32']:
-    # ExecutionEngine, no weak symbols in COFF.
-    config.available_features.add('uses_COFF')
+  # ExecutionEngine, no weak symbols in COFF.
+  config.available_features.add('uses_COFF')
 else:
-    # Others/can-execute.txt
-    config.available_features.add('can-execute')
+  # Others/can-execute.txt
+  config.available_features.add('can-execute')
 
 # Loadable module
 if config.has_plugins:
-    config.available_features.add('plugins')
+  config.available_features.add('plugins')
 
 if config.build_examples:
-    config.available_features.add('examples')
+  config.available_features.add('examples')
 
 if config.linked_bye_extension:
-    config.substitutions.append(('%llvmcheckext', 'CHECK-EXT'))
-    config.substitutions.append(('%loadbye', ''))
-    config.substitutions.append(('%loadnewpmbye', ''))
+  config.substitutions.append(('%llvmcheckext', 'CHECK-EXT'))
+  config.substitutions.append(('%loadbye', ''))
+  config.substitutions.append(('%loadnewpmbye', ''))
 else:
-    config.substitutions.append(('%llvmcheckext', 'CHECK-NOEXT'))
-    config.substitutions.append(('%loadbye',
-                                 '-load={}/Bye{}'.format(config.llvm_shlib_dir,
-                                                         config.llvm_shlib_ext)))
-    config.substitutions.append(('%loadnewpmbye',
-                                 '-load-pass-plugin={}/Bye{}'
-                                 .format(config.llvm_shlib_dir,
-                                         config.llvm_shlib_ext)))
+  config.substitutions.append(('%llvmcheckext', 'CHECK-NOEXT'))
+  config.substitutions.append((
+      '%loadbye',
+      '-load={}/Bye{}'.format(config.llvm_shlib_dir, config.llvm_shlib_ext),
+  ))
+  config.substitutions.append((
+      '%loadnewpmbye',
+      '-load-pass-plugin={}/Bye{}'.format(
+          config.llvm_shlib_dir, config.llvm_shlib_ext
+      ),
+  ))
 
 if config.linked_exampleirtransforms_extension:
-    config.substitutions.append(('%loadexampleirtransforms',''))
+  config.substitutions.append(('%loadexampleirtransforms', ''))
 else:
-    config.substitutions.append(('%loadexampleirtransforms',
-                                 '-load-pass-plugin={}/ExampleIRTransforms{}'
-                                 .format(config.llvm_shlib_dir,
-                                 config.llvm_shlib_ext)))
+  config.substitutions.append((
+      '%loadexampleirtransforms',
+      '-load-pass-plugin={}/ExampleIRTransforms{}'.format(
+          config.llvm_shlib_dir, config.llvm_shlib_ext
+      ),
+  ))
 
 # Static libraries are not built if BUILD_SHARED_LIBS is ON.
 if not config.build_shared_libs and not config.link_llvm_dylib:
-    config.available_features.add('static-libs')
+  config.available_features.add('static-libs')
 
 if config.link_llvm_dylib:
-    config.available_features.add('llvm-dylib')
-    config.substitutions.append(
-        ('%llvmdylib',
-         '{}/libLLVM-{}{}'.format(config.llvm_shlib_dir,
-                                  config.llvm_dylib_version,
-                                  config.llvm_shlib_ext)))
+  config.available_features.add('llvm-dylib')
+  config.substitutions.append((
+      '%llvmdylib',
+      '{}/libLLVM-{}{}'.format(
+          config.llvm_shlib_dir,
+          config.llvm_dylib_version,
+          config.llvm_shlib_ext,
+      ),
+  ))
 
 if config.have_tf_aot:
-    config.available_features.add("have_tf_aot")
+  config.available_features.add('have_tf_aot')
 
 if config.have_tflite:
-    config.available_features.add("have_tflite")
+  config.available_features.add('have_tflite')
 
 if config.llvm_inliner_model_autogenerated:
-    config.available_features.add("llvm_inliner_model_autogenerated")
-
-if config.llvm_inliner_model_emitc:
-    config.available_features.add("llvm_inliner_model_emitc")
+  config.available_features.add('llvm_inliner_model_autogenerated')
 
 if config.llvm_raevict_model_autogenerated:
-    config.available_features.add("llvm_raevict_model_autogenerated")
+  config.available_features.add('llvm_raevict_model_autogenerated')
+
 
 def have_cxx_shared_library():
-    readobj_exe = lit.util.which('llvm-readobj', config.llvm_tools_dir)
-    if not readobj_exe:
-        print('llvm-readobj not found')
-        return False
+  readobj_exe = lit.util.which('llvm-readobj', config.llvm_tools_dir)
+  if not readobj_exe:
+    print('llvm-readobj not found')
+    return False
 
-    try:
-        readobj_cmd = subprocess.Popen(
-            [readobj_exe, '--needed-libs', readobj_exe], stdout=subprocess.PIPE)
-    except OSError:
-        print('could not exec llvm-readobj')
-        return False
-
-    readobj_out = readobj_cmd.stdout.read().decode('ascii')
-    readobj_cmd.wait()
-
-    regex = re.compile(r'(libc\+\+|libstdc\+\+|msvcp).*\.(so|dylib|dll)')
-    needed_libs = False
-    for line in readobj_out.splitlines():
-        if 'NeededLibraries [' in line:
-            needed_libs = True
-        if ']' in line:
-            needed_libs = False
-        if needed_libs and regex.search(line.lower()):
-            return True
+  try:
+    readobj_cmd = subprocess.Popen(
+        [readobj_exe, '--needed-libs', readobj_exe], stdout=subprocess.PIPE
+    )
+  except OSError:
+    print('could not exec llvm-readobj')
     return False
 
+  readobj_out = readobj_cmd.stdout.read().decode('ascii')
+  readobj_cmd.wait()
+
+  regex = re.compile(r'(libc\+\+|libstdc\+\+|msvcp).*\.(so|dylib|dll)')
+  needed_libs = False
+  for line in readobj_out.splitlines():
+    if 'NeededLibraries [' in line:
+      needed_libs = True
+    if ']' in line:
+      needed_libs = False
+    if needed_libs and regex.search(line.lower()):
+      return True
+  return False
+
+
 if have_cxx_shared_library():
-    config.available_features.add('cxx-shared-library')
+  config.available_features.add('cxx-shared-library')
 
 if config.libcxx_used:
-    config.available_features.add('libcxx-used')
+  config.available_features.add('libcxx-used')
 
 # LLVM can be configured with an empty default triple
 # Some tests are "generic" and require a valid default triple
 if config.target_triple:
-    config.available_features.add('default_triple')
-    # Direct object generation
-    if not config.target_triple.startswith(("nvptx", "xcore")):
-        config.available_features.add('object-emission')
+  config.available_features.add('default_triple')
+  # Direct object generation
+  if not config.target_triple.startswith(('nvptx', 'xcore')):
+    config.available_features.add('object-emission')
 
 if config.have_llvm_driver:
   config.available_features.add('llvm-driver')
@@ -375,130 +477,156 @@
 
 
 def have_ld_plugin_support():
-    if not os.path.exists(os.path.join(config.llvm_shlib_dir, 'LLVMgold' + config.llvm_shlib_ext)):
-        return False
-
-    ld_cmd = subprocess.Popen(
-        [config.gold_executable, '--help'], stdout=subprocess.PIPE, env={'LANG': 'C'})
-    ld_out = ld_cmd.stdout.read().decode()
-    ld_cmd.wait()
-
-    if not '-plugin' in ld_out:
-        return False
-
-    # check that the used emulations are supported.
-    emu_line = [l for l in ld_out.split('\n') if 'supported emulations' in l]
-    if len(emu_line) != 1:
-        return False
-    emu_line = emu_line[0]
-    fields = emu_line.split(':')
-    if len(fields) != 3:
-        return False
-    emulations = fields[2].split()
-    if 'elf_x86_64' not in emulations:
-        return False
-    if 'elf32ppc' in emulations:
-        config.available_features.add('ld_emu_elf32ppc')
-
-    ld_version = subprocess.Popen(
-        [config.gold_executable, '--version'], stdout=subprocess.PIPE, env={'LANG': 'C'})
-    if not 'GNU gold' in ld_version.stdout.read().decode():
-        return False
-    ld_version.wait()
-
-    return True
+  if not os.path.exists(
+      os.path.join(config.llvm_shlib_dir, 'LLVMgold' + config.llvm_shlib_ext)
+  ):
+    return False
+
+  ld_cmd = subprocess.Popen(
+      [config.gold_executable, '--help'],
+      stdout=subprocess.PIPE,
+      env={'LANG': 'C'},
+  )
+  ld_out = ld_cmd.stdout.read().decode()
+  ld_cmd.wait()
+
+  if not '-plugin' in ld_out:
+    return False
+
+  # check that the used emulations are supported.
+  emu_line = [l for l in ld_out.split('\n') if 'supported emulations' in l]
+  if len(emu_line) != 1:
+    return False
+  emu_line = emu_line[0]
+  fields = emu_line.split(':')
+  if len(fields) != 3:
+    return False
+  emulations = fields[2].split()
+  if 'elf_x86_64' not in emulations:
+    return False
+  if 'elf32ppc' in emulations:
+    config.available_features.add('ld_emu_elf32ppc')
+
+  ld_version = subprocess.Popen(
+      [config.gold_executable, '--version'],
+      stdout=subprocess.PIPE,
+      env={'LANG': 'C'},
+  )
+  if not 'GNU gold' in ld_version.stdout.read().decode():
+    return False
+  ld_version.wait()
+
+  return True
 
 
 if have_ld_plugin_support():
-    config.available_features.add('ld_plugin')
+  config.available_features.add('ld_plugin')
 
 
 def have_ld64_plugin_support():
-    if not os.path.exists(os.path.join(config.llvm_shlib_dir, 'libLTO' + config.llvm_shlib_ext)):
-        return False
+  if not os.path.exists(
+      os.path.join(config.llvm_shlib_dir, 'libLTO' + config.llvm_shlib_ext)
+  ):
+    return False
 
-    if config.ld64_executable == '':
-        return False
+  if config.ld64_executable == '':
+    return False
 
-    ld_cmd = subprocess.Popen(
-        [config.ld64_executable, '-v'], stderr=subprocess.PIPE)
-    ld_out = ld_cmd.stderr.read().decode()
-    ld_cmd.wait()
+  ld_cmd = subprocess.Popen(
+      [config.ld64_executable, '-v'], stderr=subprocess.PIPE
+  )
+  ld_out = ld_cmd.stderr.read().decode()
+  ld_cmd.wait()
 
-    if 'ld64' not in ld_out or 'LTO' not in ld_out:
-        return False
+  if 'ld64' not in ld_out or 'LTO' not in ld_out:
+    return False
 
-    return True
+  return True
 
 
 if have_ld64_plugin_support():
-    config.available_features.add('ld64_plugin')
+  config.available_features.add('ld64_plugin')
 
 # Ask llvm-config about asserts
-llvm_config.feature_config(
-    [('--assertion-mode', {'ON': 'asserts'}),
-     ('--build-mode', {'[Dd][Ee][Bb][Uu][Gg]': 'debug'})])
+llvm_config.feature_config([
+    ('--assertion-mode', {'ON': 'asserts'}),
+    ('--build-mode', {'[Dd][Ee][Bb][Uu][Gg]': 'debug'}),
+])
 
 if 'darwin' == sys.platform:
-    cmd = ['sysctl', 'hw.optional.fma']
-    sysctl_cmd = subprocess.Popen(cmd, stdout=subprocess.PIPE)
-
-    # Non zero return, probably a permission issue
-    if sysctl_cmd.wait():
-        print(
-          "Warning: sysctl exists but calling \"{}\" failed, defaulting to no fma3.".format(
-          " ".join(cmd)))
-    else:
-        result = sysctl_cmd.stdout.read().decode('ascii')
-        if 'hw.optional.fma: 1' in result:
-            config.available_features.add('fma3')
+  cmd = ['sysctl', 'hw.optional.fma']
+  sysctl_cmd = subprocess.Popen(cmd, stdout=subprocess.PIPE)
+
+  # Non zero return, probably a permission issue
+  if sysctl_cmd.wait():
+    print(
+        'Warning: sysctl exists but calling "{}" failed, defaulting to no fma3.'
+        .format(' '.join(cmd))
+    )
+  else:
+    result = sysctl_cmd.stdout.read().decode('ascii')
+    if 'hw.optional.fma: 1' in result:
+      config.available_features.add('fma3')
 
 # .debug_frame is not emitted for targeting Windows x64, aarch64/arm64, AIX, or Apple Silicon Mac.
-if not re.match(r'^(x86_64|aarch64|arm64|powerpc|powerpc64).*-(windows-gnu|windows-msvc|aix)', config.target_triple) \
-    and not re.match(r'^arm64(e)?-apple-(macos|darwin)', config.target_triple):
-    config.available_features.add('debug_frame')
+if not re.match(
+    r'^(x86_64|aarch64|arm64|powerpc|powerpc64).*-(windows-gnu|windows-msvc|aix)',
+    config.target_triple,
+) and not re.match(r'^arm64(e)?-apple-(macos|darwin)', config.target_triple):
+  config.available_features.add('debug_frame')
 
 if config.have_libxar:
-    config.available_features.add('xar')
+  config.available_features.add('xar')
 
 if config.enable_threads:
-    config.available_features.add('thread_support')
+  config.available_features.add('thread_support')
 
 if config.have_libxml2:
-    config.available_features.add('libxml2')
+  config.available_features.add('libxml2')
 
 if config.have_curl:
-    config.available_features.add('curl')
+  config.available_features.add('curl')
 
 if config.have_httplib:
-    config.available_features.add('httplib')
+  config.available_features.add('httplib')
 
 if config.have_opt_viewer_modules:
-    config.available_features.add('have_opt_viewer_modules')
+  config.available_features.add('have_opt_viewer_modules')
 
 if config.expensive_checks:
-    config.available_features.add('expensive_checks')
+  config.available_features.add('expensive_checks')
+
+if 'MemoryWithOrigins' in config.llvm_use_sanitizer:
+  config.available_features.add('use_msan_with_origins')
 
-if "MemoryWithOrigins" in config.llvm_use_sanitizer:
-    config.available_features.add('use_msan_with_origins')
 
 def exclude_unsupported_files_for_aix(dirname):
-   for filename in os.listdir(dirname):
-       source_path = os.path.join( dirname, filename)
-       if os.path.isdir(source_path):
-           continue
-       f = open(source_path, 'r')
-       try:
-          data = f.read()
-          # 64-bit object files are not supported on AIX, so exclude the tests.
-          if ('-emit-obj' in data or '-filetype=obj' in data) and '64' in config.target_triple:
-            config.excludes += [ filename ]
-       finally:
-          f.close()
+  for filename in os.listdir(dirname):
+    source_path = os.path.join(dirname, filename)
+    if os.path.isdir(source_path):
+      continue
+    f = open(source_path, 'r')
+    try:
+      data = f.read()
+      # 64-bit object files are not supported on AIX, so exclude the tests.
+      if (
+          '-emit-obj' in data or '-filetype=obj' in data
+      ) and '64' in config.target_triple:
+        config.excludes += [filename]
+    finally:
+      f.close()
+
 
 if 'aix' in config.target_triple:
-    for directory in ('/CodeGen/X86', '/DebugInfo', '/DebugInfo/X86', '/DebugInfo/Generic', '/LTO/X86', '/Linker'):
-        exclude_unsupported_files_for_aix(config.test_source_root + directory)
+  for directory in (
+      '/CodeGen/X86',
+      '/DebugInfo',
+      '/DebugInfo/X86',
+      '/DebugInfo/Generic',
+      '/LTO/X86',
+      '/Linker',
+  ):
+    exclude_unsupported_files_for_aix(config.test_source_root + directory)
 
 # Some tools support an environment variable "OBJECT_MODE" on AIX OS, which
 # controls the kind of objects they will support. If there is no "OBJECT_MODE"
@@ -507,4 +635,4 @@
 # 32-bit and 64-bit objects by default, set the environment variable
 # "OBJECT_MODE" to 'any' by default on AIX OS.
 if 'system-aix' in config.available_features:
-    config.environment['OBJECT_MODE'] = 'any'
+  config.environment['OBJECT_MODE'] = 'any'
diff --git a/llvm/test/lit.site.cfg.py.in b/llvm/test/lit.site.cfg.py.in
--- a/llvm/test/lit.site.cfg.py.in
+++ b/llvm/test/lit.site.cfg.py.in
@@ -56,7 +56,6 @@
 config.have_tf_aot = @LLVM_HAVE_TF_AOT@
 config.have_tflite = @LLVM_HAVE_TFLITE@
 config.llvm_inliner_model_autogenerated = @LLVM_INLINER_MODEL_AUTOGENERATED@
-config.llvm_inliner_model_emitc = @LLVM_HAVE_EMITC_INLINERSIZEMODEL@
 config.llvm_raevict_model_autogenerated = @LLVM_RAEVICT_MODEL_AUTOGENERATED@
 config.expensive_checks = @LLVM_ENABLE_EXPENSIVE_CHECKS@
 config.dxil_tests = @LLVM_INCLUDE_DXIL_TESTS@