absl::StatusOr<std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
ProcessTensorSpec(mlir::TensorType type) {
  tensorflow::DataType dtype;
  TF_RETURN_IF_ERROR(
      ConvertScalarTypeToDataType(type.getElementType(), &dtype));

  if (!type.hasRank())
    return std::make_pair(dtype, tensorflow::PartialTensorShape());

  auto shape = type.getShape();
  llvm::SmallVector<int64_t, 4> dims;
  dims.assign(shape.begin(), shape.end());

    Tensor* cpu_tensor, StatusCallback done) {
  DataType dtype = EncodePrimitiveTypeAsDataType(shape_.element_type()).value();
  TensorShape tensor_shape;
  Status status = XLAShapeToTensorShape(shape_, &tensor_shape);
  if (!status.ok()) {
    done(status);
    return;
  }

  *cpu_tensor = Tensor(dtype, tensor_shape);

  status = stream_->Memcpy(cpu_tensor->data(), device_memory_base_,

  def.set_device(DEVICE_CPU);

  AttrValue valuesTypeAttr;
  SetAttrValue(values->dtype(), &valuesTypeAttr);
  (*def.mutable_attr())["T"] = valuesTypeAttr;

  def.add_input(strings::StrCat("input1: ", DataTypeString(tags->dtype())));
  def.add_input(strings::StrCat("input2: ", DataTypeString(values->dtype())));

  std::unique_ptr<OpKernel> kernel =

  // Generated from
  //
  //  TARGET_PATH=tensorflow_models/official/legacy/image_classification       \
  //  bazel run -c opt --config=cuda ${TARGET_PATH}:resnet_imagenet_main       \
  //    -- --skip_eval --num_gpus=1 --dtype=fp16 --batch_size=192              \
  //    --train_steps=210 --enable_xla --enable_eager=true
  //
  // At CL 245846452
  TF_ASSERT_OK(RunAutoClusteringTestWithPbtxt("keras_imagenet_main"));
}

  tensorflow::AsyncValueTensor* av_tensor =
      tensorflow::AsyncValueTensor::FromTensor(tensor);
  if (av_tensor == nullptr) {
    TF_ASSIGN_OR_RETURN(
        *tensor, MakeTensorFromPjRtBuffer(tensor->dtype(), tensor->shape(),
                                          std::move(buffer)));
  } else {
    av_tensor->SetBuffer(std::move(buffer));
  }
  return absl::OkStatus();
}

  NodeBuilder::NodeOut enter_input(enter_output.node(), enter_output.index());

  const int next_output_index = 0;
  DataType dtype = enter_output.node()->output_type(0);
  NodeBuilder::NodeOut next_input(NextIterationName(scope, loop_var_idx),
                                  next_output_index, dtype);

  std::vector<NodeBuilder::NodeOut> input_list({enter_input, next_input});
  const string unique_name = scope.GetUniqueNameForOp("Merge");

}

TEST(Dump, TextualIrWithOptions) {
  Graph graph(OpRegistry::Global());
  Node* node;
  TF_ASSERT_OK(NodeBuilder("A", "Placeholder")
                   .Attr("dtype", DT_FLOAT)
                   .Finalize(&graph, &node));

  string actual;
  StringWritableFile file(&actual);
  TF_ASSERT_OK(DumpTextualIRToFile(MlirDumpConfig().emit_location_information(),

    .Attr("shared_name: string = ''")
    .Attr("debug_name: string = ''")
    .Attr("dtype: type")
    .Attr("shape: shape")
    .Output("resource: resource")
    .SetIsStateful()
    .SetShapeFn([](shape_inference::InferenceContext* c) {
      c->set_output(0, c->Scalar());
      DataType t;
      TF_RETURN_IF_ERROR(c->GetAttr("dtype", &t));
      PartialTensorShape p;
      TF_RETURN_IF_ERROR(c->GetAttr("shape", &p));

    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method

    auto& var_op = std::get<0>(var_and_tensor);
    auto& resource_tensor = std::get<1>(var_and_tensor);
    bool is_variable_initialized = false;
    if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {
      is_variable_initialized = true;
    } else {
      auto handle = resource_tensor.scalar<tensorflow::ResourceHandle>()();
      is_variable_initialized =