Status TapeVSpace::CallBackwardFunction(
    const string& op_type, GradientFunction* gradient_function,
    const std::vector<int64_t>& unneeded_gradients,
    gtl::ArraySlice<AbstractTensorHandle*> output_gradients,
    absl::Span<AbstractTensorHandle*> result) const {
  if (gradient_function == nullptr) {
    return errors::InvalidArgument(
        "Provided null gradient_function for '", op_type, "'.\n",

          grad_it->second.push_back(new_gradients);
          vspace.MarkAsResult(new_gradients);
        }
        out_gradients.push_back(new_gradients);
      }
    }
    VLOG(1) << "Calling gradient function for '" << trace.op_type << "'";
    std::vector<Gradient*> in_gradients(trace.input_tensor_id.size());
    DCHECK(build_default_zeros_grads || zero_indices.empty());
    if (any_gradient_nonzero) {

limitations under the License.
==============================================================================*/
#include "tensorflow/c/experimental/gradients/math_grad.h"

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/gradients.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/experimental/ops/nn_ops.h"

        ":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 = [

            ":logging",
            ":tf_status",
            ":tf_tensor",
            "//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",

  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 {};
  }