status->status = cc_status;
    return;
  }
  if (var_info->var_info.var() == nullptr) {
    cc_status = absl::InvalidArgumentError(
        "VariableInfo does not track a resource variable.");
    status->status = cc_status;
    return;
  }

  cc_status = cc_ctx->allocate_temp(var_info->var_info.var()->tensor()->dtype(),

  return ret;
}

TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) {
  tensorflow::RunOptions options;
  if (enable_full_trace) {
    options.set_trace_level(tensorflow::RunOptions::FULL_TRACE);
  } else {
    options.set_trace_level(tensorflow::RunOptions::NO_TRACE);
  }
  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(options, ret));
  return ret;
}

    TF_OutputList* add_outputs = TF_NewOutputList();
    TF_OutputListSetNumOutputs(add_outputs, 1, status.get());
    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
    // Trace the operation now (create a node in the graph).
    TF_ExecuteOperation(add_op, 2, inputs, add_outputs, s);
    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    TF_DeleteAbstractOp(add_op);

  if (use_function) {
    const char* fn_name = "test_fn";
    core::RefCountPtr<AbstractFunction> scoped_func;
    // Returning null tensors from a tf.function is not supported, so we keep
    // track of indices in the model's outputs are nullptr in this set.
    // The FunctionDef only outputs the non-null tensors. We later pad the
    // function op outputs to have nullptrs at the `null_indices`.