// Converts a shape from MLIR to a TensorFlow tensor shape proto.
void ConvertToTensorShapeProto(llvm::ArrayRef<int64_t> shape,
                               TensorShapeProto* output_shape);

// Converts an MLIR type to a TensorFlow tensor shape.
PartialTensorShape ConvertTypeToTensorShape(const mlir::Type& type);

// Converts an MLIR shaped type to a TensorFlow shape attribute.

  values: {
    tuple_value: {
      values: {
        tensor_spec_value: {
          name : "x"
          shape: {
            dim: {
              size: 1
            }
          }
          dtype: DT_FLOAT
        }
      }
      values: {
        tensor_spec_value: {
          name : "y"
          shape: {
            dim: {
              size: 1
            }
          }
          dtype: DT_FLOAT

    {
      "builtin_code": "SPLIT"
    }
  ],
  "subgraphs": [
    {
      "tensors": [
        {
          "shape": [
            1
          ],
          "name": "split_dim",
          "quantization": {
          }
        },
        {
          "shape": [
            256,
            32,
            32,
            3
          ],
          "name": "input",

	const expectedLocation = "runtime/pprof.nonRecursiveGenericAllocFunction[go.shape.struct {},go.shape.struct { runtime/pprof.buf [128]uint8 }];runtime/pprof.nonRecursiveGenericAllocFunction[go.shape.struct...

// CHECK:           [[LHSSHAPE:%.*]] = shape.shape_of [[LHS]] : tensor<1x4x2xf32>
// CHECK:           [[RHSSHAPE:%.*]] = shape.shape_of [[RHS]] : tensor<3x2x4xf32>
// CHECK:           [[CM2:%.*]] = arith.constant -2 : index
// CHECK:           [[LHSHEAD:%.*]], [[LHSTAIL:%.*]] = "shape.split_at"([[LHSSHAPE]], [[CM2]])
// CHECK:           [[RHSHEAD:%.*]], [[RHSTAIL:%.*]] = "shape.split_at"([[RHSSHAPE]], [[CM2]])

  // This is our 1D tensor of varying dtype.
  std::vector<typename TypeParam::type> value = {42, 100, 0, 1, 4, 29};
  // Shape is Rank 1 vector.
  std::vector<int64_t> shape;
  shape.push_back(value.size());

  Tensor original_tensor = Tensor::FromBuffer(
      /*dtype=*/dtype, /*shape=*/shape,
      /*data=*/value.data(),
      /*len=*/value.size() * sizeof(typename TypeParam::type),

namespace ops {
namespace {

TEST(ResourceVariableGradTest, ReadVariableOpGrad) {
  TensorShape shape({});
  auto scope = Scope::NewRootScope();
  auto x = Placeholder(scope, DT_FLOAT, Placeholder::Shape(shape));

  auto var = VarHandleOp(scope, DT_FLOAT, shape);
  auto init = AssignVariableOp(scope, var, Const(scope, 2.0f, shape));

  auto temp = ReadVariableOp(scope, var, DT_FLOAT);

  auto y = Mul(scope, temp, x);

  // This is our 1D tensor of varying dtype.
  std::vector<typename TypeParam::type> value = {42, 100, 0, 1, 4, 29};
  // Shape is Rank 1 vector.
  std::vector<int64_t> shape;
  shape.push_back(value.size());

  Tensor tensor = Tensor::FromBuffer(
      /*dtype=*/dtype, /*shape=*/shape,
      /*data=*/value.data(),
      /*len=*/value.size() * sizeof(typename TypeParam::type),
      /*deleter=*/[](void*, size_t) {}, &status);

// Notable members of the hall of shame include:
//   - github.com/remyoudompheng/bigfft
//
// Do not remove or change the type signature.
// See go.dev/issue/67401.
//
//go:linkname addVV
//go:noescape
func addVV(z, x, y []Word) (c Word)

// subVV should be an internal detail,
// but widely used packages access it using linkname.
// Notable members of the hall of shame include:
//   - github.com/remyoudompheng/bigfft

// CHECK-NEXT:  } ],
// CHECK-NEXT:  subgraphs: [ {
// CHECK-NEXT:    tensors: [ {
// CHECK-NEXT:      shape: [ 1, 384 ],
// CHECK-NEXT:      buffer: 1,
// CHECK-NEXT:      name: "arg0",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:      },
// CHECK-NEXT:      has_rank: true
// CHECK-NEXT:    }, {
// CHECK-NEXT:      shape: [ 1, 96 ],
// CHECK-NEXT:      buffer: 2,
// CHECK-NEXT:      name: "arg1",