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_);
}

          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(),

            "//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",

// need for `Env::Default()`. At that time, we might decide to remove the need
// for `Env::Default()` altogether, but that's a different project, not in
// scope for now. I'm just mentioning this here as that transition will mean
// removal of the registration part from `Env` and adding it here instead: we
// will need tables to hold for each scheme the function tables that implement

    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

    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

// If `dx` is nullptr, the implementation will use dx of `OnesLike` for all
// shapes in `y`.
// The partial derivatives are returned in `dy`. `dy` should be allocated to
// size `nx`.
// `prefix` names the scope into which all gradients operations are being added.
// `prefix` must be unique within the provided graph otherwise this operation
// will fail. If `prefix` is nullptr, the default prefixing behaviour takes