[mlgo] Add infrastructure to use EmitC-generated models for inlining.

[jacobhegna]

Summary

Current MLGO models can be served in three ways:

1. release mode, which uses a TF AOT compiler to compile models into binary blobs and a header file. This requires a TF dependency at (LLVM) build time.
2. development mode, which loads TFLite models using the TFLite runtime dynamically by providing LLVM with a path to the .tflite file. This requires a TFLite dependency.
3. interactive mode, which fetches actions via two pipes (one for features going out, and one for actions going in).

None of these are suitable for a general clang release, where we don't assume any TF dependencies and package a model in the LLVM source code.

The EmitC serving path compiles TFLite models to pure C++ code with a mild runtime dependency. The runtime is a small set of tensor kernels necessary to run a neural net. The mlcompileropt repository contains (or at least, will eventually contain) a script which automates the process of the compiling the .tflile file through the various MLIR stages down to C++, and also embeds the C++ runtime directly in the autogenerated .cpp file. The result is that there is a single (.cpp, .h) pair which can be contained in the LLVM repository and built in a normal CMake build process, with no additional dependencies.

This patch adds two things:

1. the infrastructure to load + run EmitC-generated models for the ML inlining advisor, and
2. a "test" policy which was a TF function that always returned 1 and was fed through EmitC, and is loaded in the above framework. This is used to test the code in (1).

You can use the EmitC module inliner in opt with the flags -enable-ml-inliner=emitc -inliner-emitc-model-name=NAME_OF_MODEL. Currently the only supported model name is InlineOzTestModel.

[jacobhegna]

Fixing the error message when trying to use the emitc model when it was
not built with clang.

[jacobhegna]

Slight update to the patch; add command line selection for registered emitc
models.

[jacobhegna]

Note this still isn't ready for review, need to trim down this patch and split it up. just posting for visibility

[jacobhegna]

Clean up the code & delete non-test models.

[jacobhegna]

Ok, this is ready for review. The code which compiles the .tflite models to C++ and embeds the runtime can be found in this external PR: https://github.com/google/ml-compiler-opt/pull/215

[jacobhegna]

The phabricator interface/diff looked weird, updating diff.

[jacobhegna]

Squashing commits to clean up diff.

[jacobhegna]

Remove spurious changes.

[jacobhegna]

Add Apache 2.0 license to the autogenerated files + notice of original location.

Note to reviewers: I am not the most familiar with open source licensing and I
would like to get attribution + licensing reviewed carefully before this patch
lands.

[mtrofin]

Thanks for doing this!

High level, the dependency from model to LLVM should go away. Also emitc stuff should be implementation detail (i.e. not in any headers)

The models can then have their CMakeLists.txt, which IIUC it's the expected way for subdirectories.

llvm/include/llvm/Analysis/EmitCModelRegistry.h

55 ↗

(On Diff #516020)

StringMap?

60 ↗

(On Diff #516020)

why do we need this, wouldn't the macro below be sufficient?

llvm/include/llvm/Analysis/EmitCTensor.h

1 ↗

(On Diff #516020)

won't this be part of the model package, i.e. something that's (maybe cloned) per-model?

llvm/include/llvm/Analysis/MLInlineEmitCModel.h

1 ↗

(On Diff #516020)

Nit: isn't it "Model for inlining Oz"?

35 ↗

(On Diff #516020)

can't these not be surfaced to begin with?

llvm/lib/Analysis/MLInlinerEmitCRunner.h

27

This should be in the emit-ed code, so emitc stuff shouldn't appear in any llvm-facing APIs.

64

can't we set the buffers in the ctor once? they don't get reallocated.

llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.h

20 ↗

(On Diff #516020)

Not sure what the inheritance relationship buys us. We already have a common mechanism for referring to features by their enum-named index. This also seems to add a tight coupling between codegen and llvm.

If all we need is a way to identify groups of models in the registry, that could be done with the address of a static specific to each consumer, i.e. MLInlineAdvisor could have a static char ID = '\0' and the address of that ID is the key (like the legacy PM does things, too)

[marbre]

llvm/include/llvm/Analysis/EmitCTensor.h

1 ↗

(On Diff #516020)

I actually had the same question in mind and I am not sure if llvm/include/llvm/Analysis is a good location for this. However, we discussed some time ago if we should upstream the whole reference implementation, see https://discourse.llvm.org/t/tosa-reference-model-from-mlir-using-emitc/4799/11.

Furthermore, I don't know if it is acceptable to upstream code not licensed under Apache-2.0 WITH LLVM exceptions. The good news is, we would be able to re-license (most of) the reference implementation.

[jacobhegna]

Address comments from reviewers. The main changes are:

1. Include the python files to generate the EmitC models in this patch, instead

of in the mlcompileropt github repo.

2. Remove the global declaration of the emitc::Tensor type and make it an

implementation detail. This requires changing the signatures of the generated
models to speak in terms of T* instead of Tensor<T, ...>.

llvm/include/llvm/Analysis/EmitCModelRegistry.h

55 ↗

(On Diff #516020)

Fixed.

60 ↗

(On Diff #516020)

we need to call EmitCModelRegistry<ModelT>::get().addModel(...) before main() runs in the final linked binary. the easiest way to do that is to define a global object whose constructor runs that code - that's the point of the EmitCModelRegistrationHandle. We can't (as far as I know) just have the macro because then we wouldn't have a "context" to run the needed code (right now, that context is the constructor of ...Handle.

this is basically how cl::opt works, too.

Note that this mechanism is about to change when I update the patch. Instead of registering instantiated unique_ptrs of each model, I register a factory method which is capable of creating a MLModelRunner pointer. The reason for the change is because now that the emitc models don't share a common base class, there isn't a good way to store them all in a common registry (but we can store the MLModelRunner* because they use a subclass templated on the emitc model.

llvm/include/llvm/Analysis/EmitCTensor.h

1 ↗

(On Diff #516020)

It is true that the EmitC runtime includes all of this code. it's not quite cloned per-model right now, because in the python emitc code I rename the Tensor type to something like _UnusedTensorType.

The reason is that when EmitC generates models, the signature of the functions take Tensors for input and output. If the definition of Tensor is in a local, anonymous namespace (aka the embedded runtime), then the code won't even compile because there won't be an accessible declaration for Tensor for the model header.

There are really only two solutions to this:

1. make Tensor a type that's declared in a common LLVM header that each model can #include, and so each model will use the same shared definition of Tensor. This is what I did in this commit.

2. make the model signatures only use void* or float* and keep the definition of Tensor as an internal implementation detail.

I talked with mircea offline, and (2) seems ok for us. it avoids exposing this code outside of generated models. It weakens the type in the exported function signatures, but that's not horrible because we already deal with void* internally (and we're the only user of this modified emitc backend)

1 ↗

(On Diff #516020)

(see my above comment to mircea as well).

+1 to making the license change, that will be a blocker regardless of how we want to use the library. right now I don't use any of the Eigen code, so we can ignore that (if that was the main thing that can't be relicensed).

So, originally we were planning on putting the entire reference implementation in LLVM and having models #include it. The reason we didn't do this is essentially because we wanted two conflicting things:

1. models to maintain bit-identical stability over time - if you give it these bits as input today, it will give you the same answer today or in 6 months from today.
2. to be able to upgrade the runtime for performance/other reasons.

(2) can conflict with (1) because upgrades can create slight differences due to floating point numerical stability issues. So, instead we embed the runtime in each model individually so that new models get all the upgrades and old models get stability.

llvm/include/llvm/Analysis/MLInlineEmitCModel.h

1 ↗

(On Diff #516020)

file gone, not an issue anymore

35 ↗

(On Diff #516020)

the base model is going away, so not a problem anymore.

llvm/lib/Analysis/MLInlinerEmitCRunner.h

27

fixed. this function actually doesn't exist anymore.

64

fixed.

llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.h

20 ↗

(On Diff #516020)

fixed, but not with the static char trick. see the patch update for details

[jacobhegna]

Minor fixes.

[mtrofin]

llvm/include/llvm/Analysis/EmitCModelRegistry.h

60 ↗

(On Diff #516020)

ah, makes sense. thanks!

109 ↗

(On Diff #517643)

could this macro also #include the header - I think it'd be totally reasonable to follow a canonical naming convention where the header name is trivially derivable from EmitCModelType, for example.

llvm/include/llvm/Analysis/InlineModelFeatureMaps.h

25–48

pls add a small doc accordingly

84

can you peel this bit of the change separately and just submit it (so the part that adds the self-doc-ing and makes the 2 lists the same)

llvm/lib/Analysis/MLInlinerEmitCRunner.h

29

would it be possible for the emitC-ed code to just give us a void* lookupTensorBuffer(const std::string &) - it'd also keep the codegen-ed files smaller

llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.h

17 ↗

(On Diff #517643)

can we add a "this is generated, avoid modifying me by hand" type of thing loud somewhere in the comment? same for the .cpp

[jacobhegna]

Address most of comments; will move the feature macro stuff to a new patch in
the next update.

llvm/include/llvm/Analysis/EmitCModelRegistry.h

109 ↗

(On Diff #517643)

mmm we can if you want, but the problem is that I think we will eventually store models for different passes in different places - for example, the regalloc models might end up somewhere in llvm/lib/CodeGen. we can put the include here if you want, but not sure it's the most useful optimization

llvm/lib/Analysis/MLInlinerEmitCRunner.h

29

fixed

llvm/lib/Analysis/models/emitc/InlineOzTestModel.emitc.h

17 ↗

(On Diff #517643)

fixed

[mtrofin]

llvm/include/llvm/Analysis/EmitCModelRegistry.h

109 ↗

(On Diff #517643)

maybe add a parameter that is the header location, so this becomes 1 gesture rather than 2 (easier to discover what needs to be given, too)

llvm/lib/Analysis/MLInlinerEmitCRunner.h

29

ok but now you can avoid the macro-ing, right?

[jacobhegna]

Address comments - mainly making the emitc runner generic beyond inlining.

[jacobhegna]

Rebasing to HEAD to get inline macro changes.

llvm/include/llvm/Analysis/InlineAdvisor.h

 ///
 /// - Development mode, for training new models.
 /// In this mode, we trade off runtime performance for flexibility. This mode
 /// requires the full C Tensorflow API library, and evaluates models
 /// dynamically. This mode also permits generating training logs, for offline
 /// training.
 ///
 /// - Dynamically load an advisor via a plugin (PluginInlineAdvisorAnalysis)
-enum class InliningAdvisorMode : int { Default, Release, Development };
+enum class InliningAdvisorMode : int { Default, Release, Development, EmitC };
 
 // Each entry represents an inline driver.
 enum class InlinePass : int {
   AlwaysInliner,
   CGSCCInliner,
   EarlyInliner,
   ModuleInliner,
   MLInliner,
public:
 
   PreservedAnalyses run(Module &M, ModuleAnalysisManager &MAM);
 
   PreservedAnalyses run(LazyCallGraph::SCC &InitialC, CGSCCAnalysisManager &AM,
                         LazyCallGraph &CG, CGSCCUpdateResult &UR);
 };
 
 std::unique_ptr<InlineAdvisor>
+getEmitCModeAdvisor(Module &M, ModuleAnalysisManager &MAM);
+
+std::unique_ptr<InlineAdvisor>
 getReleaseModeAdvisor(Module &M, ModuleAnalysisManager &MAM);
 
 std::unique_ptr<InlineAdvisor>
 getDevelopmentModeAdvisor(Module &M, ModuleAnalysisManager &MAM,
                           std::function<bool(CallBase &)> GetDefaultAdvice);
 
 // Default (manual policy) decision making helper APIs. Shared with the legacy
 // pass manager inliner.

llvm/include/llvm/Analysis/InlineModelFeatureMaps.h

 #include <vector>
 
 namespace llvm {
 
 // List of cost features. A "cost" feature is a summand of the heuristic-based
 // inline cost, and we define them separately to preserve the original heuristic
 // behavior.
 #define INLINE_COST_FEATURE_ITERATOR(M)                                        \
-  M(SROASavings, "sroa_savings")                                               \
-  M(SROALosses, "sroa_losses")                                                 \
-  M(LoadElimination, "load_elimination")                                       \
-  M(CallPenalty, "call_penalty")                                               \
-  M(CallArgumentSetup, "call_argument_setup")                                  \
-  M(LoadRelativeIntrinsic, "load_relative_intrinsic")                          \
-  M(LoweredCallArgSetup, "lowered_call_arg_setup")                             \
-  M(IndirectCallPenalty, "indirect_call_penalty")                              \
-  M(JumpTablePenalty, "jump_table_penalty")                                    \
-  M(CaseClusterPenalty, "case_cluster_penalty")                                \
-  M(SwitchPenalty, "switch_penalty")                                           \
-  M(UnsimplifiedCommonInstructions, "unsimplified_common_instructions")        \
-  M(NumLoops, "num_loops")                                                     \
-  M(DeadBlocks, "dead_blocks")                                                 \
-  M(SimplifiedInstructions, "simplified_instructions")                         \
-  M(ConstantArgs, "constant_args")                                             \
-  M(ConstantOffsetPtrArgs, "constant_offset_ptr_args")                         \
-  M(CallSiteCost, "callsite_cost")                                             \
-  M(ColdCcPenalty, "cold_cc_penalty")                                          \
-  M(LastCallToStaticBonus, "last_call_to_static_bonus")                        \
-  M(IsMultipleBlocks, "is_multiple_blocks")                                    \
-  M(NestedInlines, "nested_inlines")                                           \
-  M(NestedInlineCostEstimate, "nested_inline_cost_estimate")                   \
-  M(Threshold, "threshold")
+  M(SROASavings, sroa_savings, "")                                             \
+  M(SROALosses, sroa_losses, "")                                               \
+  M(LoadElimination, load_elimination, "")                                     \
+  M(CallPenalty, call_penalty, "")                                             \
+  M(CallArgumentSetup, call_argument_setup, "")                                \
+  M(LoadRelativeIntrinsic, load_relative_intrinsic, "")                        \
+  M(LoweredCallArgSetup, lowered_call_arg_setup, "")                           \
+  M(IndirectCallPenalty, indirect_call_penalty, "")                            \
+  M(JumpTablePenalty, jump_table_penalty, "")                                  \
+  M(CaseClusterPenalty, case_cluster_penalty, "")                              \
+  M(SwitchPenalty, switch_penalty, "")                                         \
+  M(UnsimplifiedCommonInstructions, unsimplified_common_instructions, "")      \
+  M(NumLoops, num_loops, "")                                                   \
+  M(DeadBlocks, dead_blocks, "")                                               \
+  M(SimplifiedInstructions, simplified_instructions, "")                       \
+  M(ConstantArgs, constant_args, "")                                           \
+  M(ConstantOffsetPtrArgs, constant_offset_ptr_args, "")                       \
+  M(CallSiteCost, callsite_cost, "")                                           \
+  M(ColdCcPenalty, cold_cc_penalty, "")                                        \
+  M(LastCallToStaticBonus, last_call_to_static_bonus, "")                      \
+  M(IsMultipleBlocks, is_multiple_blocks, "")                                  \
+  M(NestedInlines, nested_inlines, "")                                         \
+  M(NestedInlineCostEstimate, nested_inline_cost_estimate, "")                 \
+  M(Threshold, threshold, "")
 
 // clang-format off
 enum class InlineCostFeatureIndex : size_t {
-#define POPULATE_INDICES(INDEX_NAME, NAME) INDEX_NAME,
+#define POPULATE_INDICES(INDEX_NAME, NAME, DOC) INDEX_NAME,
   INLINE_COST_FEATURE_ITERATOR(POPULATE_INDICES)
 #undef POPULATE_INDICES
 
   NumberOfFeatures
 };
 // clang-format on
 
 using InlineCostFeatures =

 // List of features. Each feature is defined through a triple:
 // - the name of an enum member, which will be the feature index
 // - a textual name, used for Tensorflow model binding (so it needs to match the
 // names used by the Tensorflow model)
 // - a documentation description. Currently, that is not used anywhere
 // programmatically, and serves as workaround to inability of inserting comments
 // in macros.
 #define INLINE_FEATURE_ITERATOR(M)                                             \
-  M(CalleeBasicBlockCount, "callee_basic_block_count",                         \
+  M(CalleeBasicBlockCount, callee_basic_block_count,                           \
     "number of basic blocks of the callee")                                    \
-  M(CallSiteHeight, "callsite_height",                                         \
+  M(CallSiteHeight, callsite_height,                                           \
     "position of the call site in the original call graph - measured from "    \
     "the farthest SCC")                                                        \
-  M(NodeCount, "node_count",                                                   \
+  M(NodeCount, node_count,                                                     \
     "total current number of defined functions in the module")                 \
-  M(NrCtantParams, "nr_ctant_params",                                          \
+  M(NrCtantParams, nr_ctant_params,                                            \
     "number of parameters in the call site that are constants")                \
-  M(CostEstimate, "cost_estimate", "total cost estimate (threshold - free)")   \
-  M(EdgeCount, "edge_count", "total number of calls in the module")            \
-  M(CallerUsers, "caller_users",                                               \
+  M(CostEstimate, cost_estimate, "total cost estimate (threshold - free)")     \
+  M(EdgeCount, edge_count, "total number of calls in the module")              \
+  M(CallerUsers, caller_users,                                                 \
     "number of module-internal users of the caller, +1 if the caller is "      \
     "exposed externally")                                                      \
-  M(CallerConditionallyExecutedBlocks, "caller_conditionally_executed_blocks", \
+  M(CallerConditionallyExecutedBlocks, caller_conditionally_executed_blocks,   \
     "number of blocks reached from a conditional instruction, in the caller")  \
-  M(CallerBasicBlockCount, "caller_basic_block_count",                         \
+  M(CallerBasicBlockCount, caller_basic_block_count,                           \
     "number of basic blocks in the caller")                                    \
-  M(CalleeConditionallyExecutedBlocks, "callee_conditionally_executed_blocks", \
+  M(CalleeConditionallyExecutedBlocks, callee_conditionally_executed_blocks,   \
     "number of blocks reached from a conditional instruction, in the callee")  \
-  M(CalleeUsers, "callee_users",                                               \
+  M(CalleeUsers, callee_users,                                                 \
     "number of module-internal users of the callee, +1 if the callee is "      \
     "exposed externally")
 
 // clang-format off
 enum class FeatureIndex : size_t {
+#define POPULATE_INDICES(INDEX_NAME, NAME, COMMENT) INDEX_NAME,
 // InlineCost features - these must come first
-#define POPULATE_INDICES(INDEX_NAME, NAME) INDEX_NAME,
   INLINE_COST_FEATURE_ITERATOR(POPULATE_INDICES)
-#undef POPULATE_INDICES
 
 // Non-cost features
-#define POPULATE_INDICES(INDEX_NAME, NAME, COMMENT) INDEX_NAME,
   INLINE_FEATURE_ITERATOR(POPULATE_INDICES)
 #undef POPULATE_INDICES
 
   NumberOfFeatures
 };
 // clang-format on
 
 constexpr FeatureIndex

llvm/include/llvm/Analysis/MLModelRunner.h

return reinterpret_cast<const T *>(
         getTensorUntyped(static_cast<size_t>(FeatureID)));
   }
 
   void *getTensorUntyped(size_t Index) { return InputBuffers[Index]; }
   const void *getTensorUntyped(size_t Index) const {
     return (const_cast<MLModelRunner *>(this))->getTensorUntyped(Index);
   }
 
-  enum class Kind : int { Unknown, Release, Development, NoOp, Interactive };
+  enum class Kind : int {
+    Unknown,
+    Release,
+    Development,
+    NoOp,
+    Interactive,
+    EmitC
+  };
   Kind getKind() const { return Type; }
   virtual void switchContext(StringRef Name) {}
 
 protected:
   MLModelRunner(LLVMContext &Ctx, Kind Type, size_t NrInputs)
       : Ctx(Ctx), Type(Type), InputBuffers(NrInputs) {
     assert(Type != Kind::Unknown);
   }

llvm/lib/Analysis/CMakeLists.txt

if (DEFINED LLVM_HAVE_TF_AOT OR LLVM_HAVE_TFLITE)
   endif()
 
   if (LLVM_HAVE_TFLITE)
     list(APPEND MLLinkDeps
       tensorflow-lite::tensorflow-lite)
   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
   AliasAnalysisEvaluator.cpp
   AliasAnalysisSummary.cpp
   AliasSetTracker.cpp
   Analysis.cpp
   AssumeBundleQueries.cpp
   AssumptionCache.cpp

llvm/lib/Analysis/InlineAdvisor.cpp

Advisor =
           return OIC.has_value();
         });
 #endif
     break;
   case InliningAdvisorMode::Release:
     LLVM_DEBUG(dbgs() << "Using release-mode inliner policy.\n");
     Advisor = llvm::getReleaseModeAdvisor(M, MAM);
     break;
+  case InliningAdvisorMode::EmitC:
+    LLVM_DEBUG(dbgs() << "Using EmitC-compiled policy.\n");
+    Advisor = llvm::getEmitCModeAdvisor(M, MAM);
   }
 
   return !!Advisor;
 }
 
 /// Return true if inlining of CB can block the caller from being
 /// inlined which is proved to be more beneficial. \p IC is the
 /// estimated inline cost associated with callsite \p CB.

llvm/lib/Analysis/MLInlineAdvisor.cpp

 #include "llvm/Analysis/MLModelRunner.h"
 #include "llvm/Analysis/OptimizationRemarkEmitter.h"
 #include "llvm/Analysis/ReleaseModeModelRunner.h"
 #include "llvm/Analysis/TargetTransformInfo.h"
 #include "llvm/IR/Dominators.h"
 #include "llvm/IR/InstIterator.h"
 #include "llvm/IR/PassManager.h"
 #include "llvm/Support/CommandLine.h"
+#include "MLInlinerEmitCRunner.h"
 
 using namespace llvm;
 
 static cl::opt<std::string> InteractiveChannelBaseName(
     "inliner-interactive-channel-base", cl::Hidden,
     cl::desc(
         "Base file path for the interactive mode. The incoming filename should "
         "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
 using CompiledModelType = llvm::InlinerSizeModel;
 #else
 using CompiledModelType = NoopSavedModelImpl;
 #endif
 
 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);
+  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>
 llvm::getReleaseModeAdvisor(Module &M, ModuleAnalysisManager &MAM) {
   if (!llvm::isEmbeddedModelEvaluatorValid<CompiledModelType>() &&
       InteractiveChannelBaseName.empty())
     return nullptr;
   std::unique_ptr<MLModelRunner> AOTRunner;
   if (InteractiveChannelBaseName.empty())
     AOTRunner = std::make_unique<ReleaseModeModelRunner<CompiledModelType>>(
         M.getContext(), FeatureMap, DecisionName);

 static cl::opt<bool> KeepFPICache(
     "ml-advisor-keep-fpi-cache", cl::Hidden,
     cl::desc(
         "For test - keep the ML Inline advisor's FunctionPropertiesInfo cache"),
     cl::init(false));
 
 // clang-format off
 const std::vector<TensorSpec> llvm::FeatureMap{
-#define POPULATE_NAMES(_, NAME) TensorSpec::createSpec<int64_t>(NAME, {1} ),
+#define POPULATE_NAMES(_, NAME, __) TensorSpec::createSpec<int64_t>(#NAME, {1} ),
 // InlineCost features - these must come first
   INLINE_COST_FEATURE_ITERATOR(POPULATE_NAMES)
-#undef POPULATE_NAMES
 
 // Non-cost features
-#define POPULATE_NAMES(_, NAME, __) TensorSpec::createSpec<int64_t>(NAME, {1} ),
   INLINE_FEATURE_ITERATOR(POPULATE_NAMES)
 #undef POPULATE_NAMES
 };
 // clang-format on
 
 const char *const llvm::DecisionName = "inlining_decision";
 const TensorSpec llvm::InlineDecisionSpec =
     TensorSpec::createSpec<int64_t>(DecisionName, {1});

llvm/lib/Analysis/MLInlinerEmitCRunner.h

+//===- MLInlinerEmitCRunner.h ---- EmitC ML model runner  -------*- 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_EMITCMODELRUNNER_H
+#define LLVM_ANALYSIS_EMITCMODELRUNNER_H
+
+#if defined(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
+
+#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) {
+  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));
+}
+
+class EmitCModelRunner : public MLModelRunner {
+public:
+  EmitCModelRunner(LLVMContext &Ctx, const std::vector<TensorSpec> &Inputs,
+                   const TensorSpec &Advice)
+      : 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);
+    }
+  }
+
+  static bool classof(const MLModelRunner *R) {
+    return R->getKind() == MLModelRunner::Kind::EmitC;
+  }
+
+  virtual ~EmitCModelRunner() {}
+
+  void *evaluateUntyped() override {
+#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>(                \
+        getTensorUntyped(idx), InputSpecs[idx]));                              \
+  } while (false);
+
+    INLINE_FEATURE_ITERATOR(FEATURE_SETTER);
+    INLINE_COST_FEATURE_ITERATOR(FEATURE_SETTER);
+#undef FEATURE_SETTER
+    Result = Model.action();
+    return static_cast<void *>(Result.get());
+  }
+
+private:
+  const std::vector<TensorSpec> InputSpecs;
+  const TensorSpec OutputSpec;
+
+  MLInlineModel Model;
+  Tensor<int64_t, 1> Result;
+};
+
+} // namespace llvm
+
+#endif // defined(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
+#endif

llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.h

+#include <cstdint>
+
+#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 = {
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+    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 = {
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (float)0.0e+00,         (float)0.0e+00,         (float)0.0e+00,
+        (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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+    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,
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+                              (float)-4.000000000e+01, (float)-4.000000000e+01,
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+                              (float)-4.000000000e+01, (float)-4.000000000e+01,
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+        (float)-1.454005540e-01, (float)8.374135200e-02,
+        (float)-1.273479010e-01, (float)-1.326998170e-01,
+        (float)-4.173532720e-01, (float)2.786041500e-01,
+        (float)3.906883000e-01,  (float)-3.440140780e-01,
+        (float)-1.630935370e-01, (float)-1.279881300e-01,
+        (float)-2.115882780e-01, (float)-2.303743810e-01,
+        (float)1.683009860e-01,  (float)4.120436610e-01,
+        (float)2.626100480e-01,  (float)2.778919640e-01,
+        (float)3.377864060e-01,  (float)8.225002880e-02,
+        (float)1.469960960e-01,  (float)-1.747772840e-01,
+        (float)3.403380810e-01,  (float)2.568980160e-01,
+        (float)-2.144733520e-01, (float)4.541271630e-01,
+        (float)-1.794124250e-01, (float)1.637876180e-01,
+        (float)-2.590546010e-01, (float)-6.642739770e-01,
+        (float)3.656847180e-01,  (float)1.298068760e-01,
+        (float)5.232693630e-02,  (float)-4.279178080e-01,
+        (float)-2.851843830e-01, (float)1.670745610e-01,
+        (float)-4.303287860e-01, (float)-4.079115390e-01,
+        (float)4.602125290e-01,  (float)-5.974282620e-01,
+        (float)1.458881200e-01,  (float)-6.717157360e-01,
+        (float)-6.618970630e-01, (float)5.428814790e-03,
+        (float)-4.026044610e-01, (float)-2.305841890e-01,
+        (float)-7.072991130e-01, (float)-1.171246100e-01,
+        (float)2.452067140e-01,  (float)-2.774640620e-01,
+        (float)-1.671426300e-01, (float)-4.060022230e-01,
+        (float)-4.236065450e-01, (float)-5.231152180e-01,
+        (float)2.592145840e-02,  (float)6.514772180e-01,
+        (float)-7.078727240e-03, (float)4.641992750e-01,
+        (float)3.235935870e-01,  (float)-1.293828490e-01,
+        (float)-2.209510210e-01, (float)1.755362000e-01,
+        (float)2.179742010e-01,  (float)-5.833495780e-02,
+        (float)-8.300729990e-01, (float)5.611345920e-02,
+        (float)-4.962053300e-01, (float)6.439535620e-01,
+        (float)-6.363400220e-01, (float)-8.765996090e-01,
+        (float)5.768699200e-02,  (float)1.422120930e-01,
+        (float)9.066201750e-04,  (float)-7.595893140e-01,
+        (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;
+  }
+};

llvm/lib/Passes/PassBuilderPipelines.cpp

 static cl::opt<InliningAdvisorMode> UseInlineAdvisor(
     "enable-ml-inliner", cl::init(InliningAdvisorMode::Default), cl::Hidden,
     cl::desc("Enable ML policy for inliner. Currently trained for -Oz only"),
     cl::values(clEnumValN(InliningAdvisorMode::Default, "default",
                           "Heuristics-based inliner version"),
                clEnumValN(InliningAdvisorMode::Development, "development",
                           "Use development mode (runtime-loadable model)"),
                clEnumValN(InliningAdvisorMode::Release, "release",
-                          "Use release mode (AOT-compiled model)")));
+                          "Use release mode (AOT-compiled model)"),
+               clEnumValN(InliningAdvisorMode::EmitC, "emitc",
+                          "Use EmitC-compiled model")));
 
 static cl::opt<bool> EnableSyntheticCounts(
     "enable-npm-synthetic-counts", cl::Hidden,
     cl::desc("Run synthetic function entry count generation "
              "pass"));
 
 /// Flag to enable inline deferral during PGO.
 static cl::opt<bool>

llvm/test/Transforms/Inline/ML/ml-test-emitc-mode.ll

+; The default inliner doesn't elide @adder, it believes it's too costly to inline 
+; adder into switcher. The ML inliner carries out that inlining, resulting in
+; a smaller result (part of it is that adder gets elided).
+;
+; 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=default -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=DEFAULT

llvm/test/lit.cfg.py

if config.have_tf_aot:
     config.available_features.add("have_tf_aot")
 
 if config.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")
+
 if config.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

llvm/test/lit.site.cfg.py.in

 config.have_opt_viewer_modules = @LLVM_HAVE_OPT_VIEWER_MODULES@
 config.libcxx_used = @LLVM_LIBCXX_USED@
 config.has_plugins = @LLVM_ENABLE_PLUGINS@
 config.linked_bye_extension = @LLVM_BYE_LINK_INTO_TOOLS@
 config.linked_exampleirtransforms_extension = @LLVM_EXAMPLEIRTRANSFORMS_LINK_INTO_TOOLS@
 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@
 config.have_llvm_driver = @LLVM_TOOL_LLVM_DRIVER_BUILD@
 
 import lit.llvm
 lit.llvm.initialize(lit_config, config)
 
 # Let the main config do the real work.
 lit_config.load_config(
     config, os.path.join(config.llvm_src_root, "test/lit.cfg.py"))