TF_ASSERT_OK_AND_ASSIGN(auto stream, executor->CreateStream());

  se::DeviceMemoryBase gpu_dst{device_tensor.data(), 4 * sizeof(float)};
  xla::Shape shape;
  TF_ASSERT_OK(TensorShapeToXLAShape(DT_FLOAT, TensorShape({2, 2}), &shape));

  // Copy the cpu_tensor to the GPU first before trying to copy it back.
  TF_ASSERT_OK(
      stream->Memcpy(&gpu_dst, origin_cpu_tensor.data(), gpu_dst.size()));

      TensorShape shape;
      TF_RETURN_IF_ERROR(XLAShapeToTensorShape(subshape, &shape));
      output_tensor_shapes.push_back(shape);
    }
  } else {
    for (int i = 0; i < ctx->num_outputs(); ++i) {
      output_tensor_shapes.push_back(compilation_result->outputs[i].shape);
    }
  }

  // Copy XLA results to the OpOutputList.
  int output_num = 0;
  for (int i = 0, end = ctx->num_outputs(); i < end; ++i) {

}
func.func @WhitespaceTokenize_RaggedConcat_assert_equal_1_Assert_AssertGuard_false_3210(%arg0: tensor<i1>, %arg1: tensor<i64>, %arg2: tensor<i64>) -> tensor<i1> attributes {tf._input_shapes = [#tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>], tf.signature.is_stateful} {
  %0 = "tf.Const"() {value = dense<"Input tensors have incompatible shapes."> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>

import (
	"internal/abi"
	"unsafe"
)

// Should be a built-in for unsafe.Pointer?
//
// add should be an internal detail,
// but widely used packages access it using linkname.
// Notable members of the hall of shame include:
//   - fortio.org/log
//
// Do not remove or change the type signature.
// See go.dev/issue/67401.
//
//go:linkname add
//go:nosplit
func add(p unsafe.Pointer, x uintptr) unsafe.Pointer {

      std::vector<int64_t> shape;
      shape.push_back(static_cast<int64_t>(data.size()));
      attributes.emplace_back(builder.getNamedAttr(
          "window_strides", BuildVhloTensorV1Attr(shape, data, builder)));
    }
    if (!(op->padding.empty())) {
      std::vector<int64_t> shape;
      shape.push_back(static_cast<int64_t>(op->padding.size()) / 2);
      shape.push_back(2);

            tensor_spec.TensorSpec(
                shape=shape, dtype=dtypes.float32, name='input_tensor'
            )
        ),
    )
    return model

  # Prepares sample einsum input data shapes.
  # This function returns:
  # 1. Shape for input 1
  # 2. Shape for input 2
  # 3. Shape for bias
  # 4. Signature for input 1 (Could contain None dimension)

}

// Gets the shape of operand, assuming it is a dynamic shape with static rank.
Value getShapeValue(Location loc, Value operand, PatternRewriter &rewriter) {
  RankedTensorType resultType =
      mlir::dyn_cast<RankedTensorType>(operand.getType());
  return rewriter.create<shape::ShapeOfOp>(
      loc,
      RankedTensorType::get(/*shape=*/{resultType.getRank()},

				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[0].utilization",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[1].score",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[2].score",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[3].utilization",
				},
			}),
		},
	}

		// UNC Absolute
		{`\\srv\share\long`, `\\?\UNC\srv\share\long`},
		{`//srv/share/long`, `\\?\UNC\srv\share\long`},
		{`/\srv/share/long`, `\\?\UNC\srv\share\long`},
		{`\\srv\share\long\`, `\\?\UNC\srv\share\long\`},
		{`\\srv\share\bar\.\long`, `\\?\UNC\srv\share\bar\long`},
		{`\\srv\share\bar\..\long`, `\\?\UNC\srv\share\long`},
		{`\\srv\share\bar\..\..\long`, `\\?\UNC\srv\share\long`}, // share name is not removed by ".."

// Converts an TensorFlow shape to the one used in MLIR.
void ConvertToMlirShape(const TensorShape& input_shape,
                        llvm::SmallVectorImpl<int64_t>* shape);

// Converts an TensorFlow shape proto to the one used in MLIR.
Status ConvertToMlirShape(const TensorShapeProto& input_shape,
                          llvm::SmallVectorImpl<int64_t>* shape);