namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {
    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),

  // CHECK-SAME: recv_key = "host_compute_channel_recv"
  // CHECK-SAME: send_key = "host_compute_channel_send"
  // CHECK-SAME: shape_inference_graph = @host_func
  // CHECK-SAME: shapes = [#tf_type.shape<*>, #tf_type.shape<3x?>]
  // CHECK-SAME: tpu_core = 0 : i64
  // CHECK: func @host_func
  // CHECK: %[[RECV_OUTPUT:[0-9]*]]:2 = "tf._XlaRecvAtHost"
  // CHECK-SAME: key = "host_compute_channel_send"

// 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.

	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),

  // 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: %[[CUSTOM_AGGREGATOR_2:.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%[[XLA_CALL_MODULE:.*]])

// Given a tensor of `shape` and `type`, as what shape should it be stored on
// the TPU device? This function tranposes or flattens the excessively-padded
// tensors to rank 1, but leaves other tensor shapes alone.
absl::StatusOr<xla::Shape> TpuShapeRepresentation(
    const TensorShape& shape, DataType type, bool use_fast_memory,
    XlaLayoutPreference layout_preference) {
  xla::Shape xla_shape;
  TF_RETURN_IF_ERROR(