const tensorflow::Scope& scope,
          const std::vector<tensorflow::Output>& inputs,
          std::vector<tensorflow::Output>* outputs) {
        DCHECK_EQ(scope.graph(), &parent->graph);
        TF_RETURN_IF_ERROR(
            CopyGraph(&params->body_graph->graph, &parent->graph,
                      &parent->refiner, params->body_inputs, inputs,
                      scope.impl()->name(), scope.impl()->control_deps(),

TEST_F(CApiGradientsTest, GradientsPrefix_TwoGradientsInSameScope) {
  BuildGraphAndAddGradientsWithPrefixes("scope/gradients", "scope/gradients_1");
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
}

TEST_F(CApiGradientsTest, GradientsPrefix_TwoGradientsInDifferentScopes) {
  BuildGraphAndAddGradientsWithPrefixes("scope/gradients", "scope_1/gradients");
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
}

  if (UseFunction()) {
    // TODO(b/168850692): Enable this.
    GTEST_SKIP() << "Can't take gradient of "
                    "SparseSoftmaxCrossEntropyWithLogits in tracing mode.";
  }

  // Score
  float X_vals[] = {1.0f, 2.0f, 3.0f, -5.0f, -4.0f, -3.0f, 2.0f, 0.0f, -1.0f};
  int64_t X_dims[] = {3, 3};
  AbstractTensorHandlePtr X;
  {
    AbstractTensorHandle* X_raw;

    assignment to variables. In particular one can write
    `var[1:3].assign([1,11,111])`.
*   Deprecated `tf.op_scope` and `tf.variable_op_scope` in favor of a unified
    `tf.name_scope` and `tf.variable_scope`. The new argument order of
    `tf.variable_scope` is incompatible with previous versions.
*   Introducing `core/util/tensor_bundle` module: a module to efficiently

            "//tensorflow/c/experimental/filesystem:modular_filesystem",
            "//tensorflow/cc:grad_ops",
            "//tensorflow/cc:gradients",
            "//tensorflow/cc:ops",
            "//tensorflow/cc:scope_internal",
            "//tensorflow/cc:while_loop",
            "//tensorflow/cc/saved_model:loader_lite",
            "//tensorflow/compiler/mlir/tfr:graph_decompose_pass",

    roots = [
        "//tensorflow/c:c_api",
        "//tensorflow/c/eager:c_api",
        "//tensorflow/cc:cc_ops",
        "//tensorflow/cc:client_session",
        "//tensorflow/cc:scope",
        "//tensorflow/core:tensorflow",
        "//tensorflow/core/data:standalone",
        # Exports for pywrap_tensorflow_internal. Many of these are transitive

// efficiency of forward-mode autodiff. Instead, language bindings should call
// DeleteGradient as soon as a Tensor which was `Watch`ed or was an output
// Tensor passed to Accumulate goes out of scope.
//
// Not thread-safe.
template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class ForwardAccumulator {
 public:
  // Does not take ownership of `vspace`, which must outlive the

                absl::Span<AbstractTensorHandle*> outputs, bool use_function) {
  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

  auto* rewrite_options =
      config->mutable_graph_options()->mutable_rewrite_options();
  rewrite_options->set_scoped_allocator_optimization(
      tensorflow::RewriterConfig::ON);
  rewrite_options->mutable_scoped_allocator_opts()->add_enable_op(
      "CollectiveReduce");

  if ((tensorflow::IsGoogleCudaEnabled() || tensorflow::IsBuiltWithROCm()) &&
      num_virtual_gpus > 0) {