diff --git a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
--- a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
+++ b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
@@ -1298,6 +1298,12 @@
       "ArrayRef<OpFoldResult>":$low, "ArrayRef<OpFoldResult>":$high,
       CArg<"bool", "false">:$nofold,
       CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs)>,
+    // Build a PadOp with constant padding,  mixed static and dynamic entries
+    // and custom result type. If the type passed is nullptr, it is inferred.
+    OpBuilder<(ins "Type":$resultType, "Value":$source,
+      "ArrayRef<OpFoldResult>":$low, "ArrayRef<OpFoldResult>":$high,
+      "Value":$constantPadValue, CArg<"bool", "false">:$nofold,
+      CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs)>
   ];
 
   let hasCanonicalizer = 1;
diff --git a/mlir/include/mlir/Dialect/Tensor/Utils/Utils.h b/mlir/include/mlir/Dialect/Tensor/Utils/Utils.h
--- a/mlir/include/mlir/Dialect/Tensor/Utils/Utils.h
+++ b/mlir/include/mlir/Dialect/Tensor/Utils/Utils.h
@@ -21,13 +21,6 @@
 PadOp createPadHighOp(RankedTensorType type, Value source, Value pad,
                       bool nofold, Location loc, OpBuilder &builder);
 
-// Return a PadOp that pads `source to `type` size with `pad` value.
-// I.e., a block will be created and the `pad` value will be yielded
-// directly. If the type passed is nullptr, it is inferred.
-PadOp createPadScalarOp(Type type, Value source, Value pad,
-                        ArrayRef<OpFoldResult> low, ArrayRef<OpFoldResult> high,
-                        bool nofold, Location loc, OpBuilder &builder);
-
 // Creates dim ops for each dynamic dimension of the ranked tensor argument and
 // returns these as values.
 SmallVector<Value> createDynamicDimValues(OpBuilder &b, Location loc,
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -2000,9 +2000,8 @@
       highValues.push_back(highVal);
     }
 
-    auto newPadOp = tensor::createPadScalarOp(
-        padOp.getType(), input, padConstant, lowValues, highValues,
-        /*nofold=*/false, loc, rewriter);
+    auto newPadOp = rewriter.create<tensor::PadOp>(
+        loc, padOp.getType(), input, lowValues, highValues, padConstant);
 
     rewriter.replaceOp(padOp, newPadOp.getResult());
     return success();
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -58,10 +58,9 @@
 
   Value padValue = rewriter.create<arith::ConstantOp>(loc, padAttr);
 
-  return tensor::createPadScalarOp(RankedTensorType::get(paddedShape, inputETy),
-                                   input, padValue, lowIndices, highIndices,
-                                   /*nofold=*/false, loc, rewriter)
-      .getResult();
+  return rewriter.create<tensor::PadOp>(
+      loc, RankedTensorType::get(paddedShape, inputETy), input, lowIndices,
+      highIndices, padValue);
 }
 
 static mlir::Value reifyConstantDim(Attribute attr,
diff --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
--- a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -2475,12 +2475,32 @@
   if (!resultType) {
     resultType = PadOp::inferResultType(sourceType, staticLow, staticHigh);
   }
+  assert(resultType.isa<RankedTensorType>());
   build(b, result, resultType, source, dynamicLow, dynamicHigh,
         b.getI64ArrayAttr(staticLow), b.getI64ArrayAttr(staticHigh),
         nofold ? b.getUnitAttr() : UnitAttr());
   result.addAttributes(attrs);
 }
 
+void PadOp::build(OpBuilder &b, OperationState &result, Type resultType,
+                  Value source, ArrayRef<OpFoldResult> low,
+                  ArrayRef<OpFoldResult> high, Value constantPadValue,
+                  bool nofold, ArrayRef<NamedAttribute> attrs) {
+  build(b, result, resultType, source, low, high, nofold, attrs);
+
+  // Add a region and a block to yield the pad value.
+  Region *region = result.regions[0].get();
+  int sourceRank = source.getType().cast<RankedTensorType>().getRank();
+  SmallVector<Type> blockArgTypes(sourceRank, b.getIndexType());
+  SmallVector<Location> blockArgLocs(sourceRank, result.location);
+
+  // `builder.createBlock` changes the insertion point within the block. Create
+  // a guard to reset the insertion point of the builder after it is destroyed.
+  OpBuilder::InsertionGuard guard(b);
+  b.createBlock(region, region->end(), blockArgTypes, blockArgLocs);
+  b.create<tensor::YieldOp>(result.location, constantPadValue);
+}
+
 llvm::SmallBitVector PadOp::getPaddedDims() {
   llvm::SmallBitVector paddedDims(getSourceType().getRank());
   auto extractPaddedDims = [&](ArrayRef<OpFoldResult> paddingWidths) {
diff --git a/mlir/lib/Dialect/Tensor/Utils/Utils.cpp b/mlir/lib/Dialect/Tensor/Utils/Utils.cpp
--- a/mlir/lib/Dialect/Tensor/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Tensor/Utils/Utils.cpp
@@ -18,24 +18,6 @@
 using namespace mlir;
 using namespace mlir::tensor;
 
-PadOp mlir::tensor::createPadScalarOp(Type type, Value source, Value pad,
-                                      ArrayRef<OpFoldResult> low,
-                                      ArrayRef<OpFoldResult> high, bool nofold,
-                                      Location loc, OpBuilder &builder) {
-  auto padTensorOp =
-      builder.create<PadOp>(loc, type, source, low, high, nofold);
-  int rank = padTensorOp.getResultType().getRank();
-  SmallVector<Type> blockArgTypes(rank, builder.getIndexType());
-  SmallVector<Location> blockArgLocs(rank, loc);
-  auto &region = padTensorOp.getRegion();
-  // `builder.createBlock` changes the insertion point within the block. Create
-  // a guard to reset the insertion point of the builder after it is destroyed.
-  OpBuilder::InsertionGuard guard(builder);
-  builder.createBlock(&region, region.end(), blockArgTypes, blockArgLocs);
-  builder.create<YieldOp>(loc, pad);
-  return padTensorOp;
-}
-
 PadOp mlir::tensor::createPadHighOp(RankedTensorType type, Value source,
                                     Value pad, bool nofold, Location loc,
                                     OpBuilder &b) {
@@ -53,7 +35,7 @@
     high[en.index()] =
         makeComposedAffineApply(b, loc, en.value() - d0, {dimOp}).getResult();
   }
-  return createPadScalarOp(type, source, pad, low, high, nofold, loc, b);
+  return b.create<PadOp>(loc, type, source, low, high, pad, nofold);
 }
 
 SmallVector<Value> mlir::tensor::createDynamicDimValues(OpBuilder &b,