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_);
}

        ":c_api_unified_internal",
        "//tensorflow/c/experimental/gradients/tape:tape_operation",
        "//tensorflow/core/lib/llvm_rtti",
        "//tensorflow/core/platform:errors",
    ],
)

cc_library(
    name = "gradients_internal",
    srcs = [
        "gradients.cc",
    ],
    hdrs = [
        "gradients.h",
        "gradients_internal.h",
    ],
    visibility = [

    }

    // Update g->name_map with the name_map from the scope, which will contain
    // the new gradient ops.
    for (int i = first_new_node_id; i < g->graph.num_node_ids(); ++i) {
      Node* n = g->graph.FindNodeId(i);
      if (n == nullptr) continue;

      // Adding the gradients to the graph can alter the prefix to prevent
      // name collisions only if this prefix has not been provided explicitly

    tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization,
    conjugate gradients and CGLS.
*   Added gradients for `matrix_solve_ls` and `self_adjoint_eig`.
*   Large cleanup to add second order gradient for ops with C++ gradients and
    improve existing gradients such that most ops can now be differentiated
    multiple times.
*   Added a solver for ordinary differential equations,

  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());
  status->status = g->graph.AddGradientDef(std::move(gdef));
}

int TF_GraphNumFunctions(TF_Graph* g) {
  tensorflow::mutex_lock l(g->mu);

    status->status = tensorflow::errors::Internal(
        "Invalid text proto for GraphDef: ", text_proto);
    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(
        "GradientDef is not supported in reading Dataset related functions: ",
        text_proto);
    return {};
  }