}

  SmallVector<int64_t> shape;
  bool refined_shape = false;
  // Build the shape of the refined type, if lhs is unranked it
  // will be directly the shape of the refined type, otherwise we merged by
  // taking the most specialized. This combines `10x?x?` and `?x?x8` into
  // `10x?x8`.
  if (!lhs_shape_type.hasRank()) {
    if (rhs_shape_type.hasRank()) {
      shape.append(rhs_shape_type.getShape().begin(),

namespace tensorflow {

std::string ImmediateExecutionTensorHandle::DebugString() const {
  PartialTensorShape shape;
  std::string shape_string;
  if (Shape(&shape).ok()) {
    shape_string = shape.DebugString();
  } else {
    shape_string = "<error computing shape>";
  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }

  auto c = ops::Add(root.WithOpName("C"), a, b);
  auto d = ops::Neg(root.WithOpName("D"), c);

  a.node()->AddAttr("_index", 0);
  b.node()->AddAttr("_index", 1);

  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
  TF_CHECK_OK(root.ToGraph(graph.get()));

  std::map<int, InferredShape> arg_shapes;
  arg_shapes[0].shape = TensorShape({2, 3});
  arg_shapes[1].shape = TensorShape({2, 3});

 protected:
  StridedArrayViewBase(ArrayRef<int64_t> shape, ArrayRef<int64_t> index,
                       int64_t axis) {
    assert(shape.size() == index.size());
    assert(axis < shape.size());
    assert(axis >= 0);
    assert(index[axis] == 0);
    offset_ = IndexToOffset(shape, index);
    stride_ = StrideForAxis(shape, axis);
    size_ = shape[axis];
  }

  // Returns the size of the 1-d slice across the tensor.

  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 {

  StringRef getArgument() const final {
    return "quant-duplicate-shape-determining-constants";
  }

  StringRef getDescription() const final {
    return "Duplicates shape-determining constants. A shape-determining "
           "constant is a constant that are transitively used to change or "
           "determine the shape of a tensor. For example, the second argument "

// 1. does not cause an error
// 2. preserves dtype and shape.
TEST_P(SavedVariableLoadingTest, LoadSavedVariableSuccessful) {
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));

  SavedVariable saved_variable;
  saved_variable.set_dtype(dtype);
  shape.AsProto(saved_variable.mutable_shape());

  std::unique_ptr<Variable> var;