TFE_OpAddAttrs(op, attributes);
  TFE_OpSetDevice(op, dev->underlying_device.c_str(), s);
  if (TF_GetCode(s) != TF_OK) return;
  int num_inputs = TFE_OpGetFlatInputCount(original_op, s);
  if (TF_GetCode(s) != TF_OK) return;
  for (int j = 0; j < num_inputs; ++j) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, j, s);
    if (TF_GetCode(s) != TF_OK) return;

               absl::StatusMessageAsCStr(status));
}

void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,
                         TF_AbstractTensor* const* inputs, TF_OutputList* o,
                         TF_Status* s) {
  for (int i = 0; i < num_inputs; i++) {
    tsl::Set_TF_Status_from_Status(s, unwrap(op)->AddInput(unwrap(inputs[i])));
    if (TF_GetCode(s) != TF_OK) {
      return;

  ASSERT_TRUE(status.ok()) << status.ToString();

  EXPECT_EQ("XTimesY", kernel_->name());
  EXPECT_EQ("XTimesY", kernel_->type_string());

  EXPECT_EQ(2, kernel_->num_inputs());
  EXPECT_EQ(DT_FLOAT, kernel_->input_type(0));
  EXPECT_EQ(DT_RESOURCE, kernel_->input_type(1));
  EXPECT_EQ(DEVICE_MEMORY, kernel_->input_memory_types()[0]);
  EXPECT_EQ(HOST_MEMORY, kernel_->input_memory_types()[1]);

  // grad = [[g_1, g_2], [g_3, g_4], [g_5, g_6]]

  // indices and data are two equal-sized lists passed
  // into DynamicStitch.
  // num_values = 2
  int32_t num_values = op.num_inputs() / 2;

  // Stop propagation along the indices list
  for (int32_t i = 0; i < num_values; i++) {
    grad_outputs->push_back(NoGradient());
  }

// returned through the provided TF_OutputList.
// Any active tape will observe the effects of this execution.
void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,
                         TF_AbstractTensor* const* inputs, TF_OutputList* o,
                         TF_Status* s);

// Creates a new TF_AbstractFunction from the current tracing states in the

        absl::AbortedError(
            "The `ids` and `calibration_methods` must have the same size."));

    // Check the number and type of inputs.
    OP_REQUIRES(context, context->num_inputs() == ids_.size() * 3,
                absl::AbortedError("The number of inputs must be  three times "
                                   "the size of the `ids` list."));
    for (int i = 0; i < ids_.size(); ++i) {

  }
  return absl::OkStatus();
}

std::vector<int> GetResourceVariableIndicesFromContext(OpKernelContext* ctx) {
  std::vector<int> out;
  for (int64 i = 0; i < ctx->num_inputs(); i++) {
    if (ctx->input(i).dtype() == DT_RESOURCE) {
      out.push_back(i);
    }
  }
  return out;
}

Status CreateVariableInfoLookup(
    absl::Span<VariableInfo const> variable_args,

  const std::vector<ArgView> &Inputs() const;
  const std::vector<ArgView> &Outputs() const;
  const std::vector<AttrView> &Attributes() const;
  const std::vector<OpArgumentView> &AllArguments() const;

  int NumInputs() const;
  int NumOutputs() const;
  ArgView OnlyInput() const;
  ArgView OnlyOutput() const;

  string FunctionName() const;
  string VariableName() const;
  string OpNameString() const;

const std::vector<AttrView>& OpView::Attributes() const {
  return argument_attrs_;
}

const std::vector<OpArgumentView>& OpView::AllArguments() const {
  return all_arguments_;
}

int OpView::NumInputs() const { return input_args_.size(); }

int OpView::NumOutputs() const { return output_args_.size(); }

ArgView OpView::OnlyInput() const {
  CHECK_EQ(input_args_.size(), 1);  // Crash OK
  return input_args_.front();