// `gradient_checker` only works with model that returns only 1 tensor.
  // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the
  // second tensor isn't needed for computing gradient so we could safely drop
  // it.
  outputs[0] = loss;
  backprop->Unref();
  return absl::OkStatus();
}

Status BiasAddModel(AbstractContext* ctx,
                    absl::Span<AbstractTensorHandle* const> inputs,

        including complex types (if supported): `tf.math.segment_prod`,
        `tf.math.segment_sum`, `tf.math.unsorted_segment_mean`,
        `tf.math.unsorted_segment_sqrt_n`, `tf.math.unsorted_segment_prod`,
        `tf.math.unsorted_segment_sum`, and therefore also
        `tf.convert_to_tensor` when `value` is of type `tf.IndexedSlices` (such
        as in the back prop though `tf.gather` into a dense embedding). See

  TF_RETURN_IF_ERROR(
      ops::IdentityN(ctx, inputs, absl::MakeSpan(temp_outputs), "IdentityN"));
  // Although, `ops::IdentityN` returns 2 tensors, the first tensor isn't needed
  // for computing gradient so we could safely drop it.
  outputs[0] = temp_outputs[1];
  temp_outputs[0]->Unref();
  return absl::OkStatus();
}

class CppGradients
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:

#     flag_values = {":tf_no_prod_deps": "True"},
# )
#
# selects.config_setting_group(
#     name = "no_prod_deps",
#     match_any = [
#         ":no_prod_deps_define",
#         ":no_prod_deps_flag",
#     ],
# )
#
# config_setting(
#     name = "no_prod_deps_cuda",
#     define_values = {
#         "tf_no_prod_deps": "1",
#         "GOOGLE_CUDA_COMPILER": "clang",
#     },

  if not paths:
    raise FileNotFoundError(
        "Can't find 'find_cuda_config.py' script inside working directory")
  proc = subprocess.Popen(
      [environ_cp['PYTHON_BIN_PATH'], paths[0]] + cuda_libraries,
      stdout=subprocess.PIPE,
      env=maybe_encode_env(environ_cp))

  if proc.wait():
    # Errors from find_cuda_config.py were sent to stderr.
    print('Asking for detailed CUDA configuration...\n')