TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ &input, /*input_values*/ &tensor, /*ninputs*/ 1,
                // output related parameters
                /*outputs*/ nullptr, /*output_values*/ nullptr, /*noutputs*/ 0,
                /*targets*/ &enqueue_op, /*ntargets*/ 1,
                /*run_metadata*/ nullptr, status);
  VLOG(1) << "Enqueuing is done.";
}

  delete[] params->body_inputs;
  delete[] params->body_outputs;
}

TF_WhileParams EmptyWhileParams() {
  return {0,       nullptr, nullptr, {nullptr, 0},
          nullptr, nullptr, nullptr, nullptr};
}

}  // namespace

TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs, int ninputs,
                           TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)

};

// Gets tensor from eager tensor handle.
const Tensor* GetTensorFromHandle(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  tensorflow::TensorHandle* handle =
      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
  if (handle->Type() != TensorHandle::LOCAL) {

  if (default_factory) {
    return default_factory(fn_name, s);
  }
  tsl::Set_TF_Status_from_Status(
      s, errors::FailedPrecondition("default_factory is nullptr"));
  return nullptr;
}

}  // end namespace tracing
}  // end namespace tensorflow

// =============================================================================
// Public C API entry points
//

      }
      for (Gradient* grad : out_gradients) {
        if (grad != nullptr) {
          vspace.DeleteGradient(grad);
        }
      }
    }
    for (int i = 0, end = in_gradients.size(); i < end; ++i) {
      const int64_t id = trace.input_tensor_id[i];
      if (in_gradients[i] != nullptr) {
        auto& unaggregated_grads = gradients[id];
        unaggregated_grads.push_back(in_gradients[i]);

  // This test is interesting because the current implementation of GradientTape
  // would return [0, 1] whereas we use build_default_zeros_grads=false here
  // so we get back [nullptr, 1].

  AbstractTensorHandlePtr x1;
  {
    AbstractTensorHandle* x1_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 1.0f, &x1_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();

  AbstractContextPtr ctx;
  {
    AbstractContext* ctx_raw = nullptr;
    Status s =
        BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    ctx.reset(ctx_raw);
  }

  AbstractTensorHandlePtr x;
  {
    AbstractTensorHandle* x_raw = nullptr;
    Status s = TestScalarTensorHandle<float, TF_FLOAT>(ctx.get(), 1.0f, &x_raw);

  TF_Status* status = TF_NewStatus();

  TF_DeleteStatus(nullptr);
  TF_DeleteBuffer(nullptr);
  TF_DeleteTensor(nullptr);
  TF_DeleteSessionOptions(nullptr);
  TF_DeleteGraph(nullptr);
  TF_DeleteImportGraphDefOptions(nullptr);
  TF_DeleteImportGraphDefResults(nullptr);
  TF_DeleteFunction(nullptr);
  TF_DeleteSession(nullptr, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  StatusPtr first_bad_status(nullptr);

  for (const auto& dt : device_threads_) {
    StatusPtr async_wait_status(TF_NewStatus());
    dt->AsyncWait(async_wait_status.get());
    // Prefer non cancelled errors to uncover real failures.
    if (TF_GetCode(async_wait_status.get()) != TF_OK &&
        (first_bad_status == nullptr ||
         TF_GetCode(first_bad_status.get()) == TF_CANCELLED)) {

  absl::Status cc_status;
  if (var_info == nullptr) {
    cc_status = absl::InvalidArgumentError("TF_VariableInfo is NULL.");
    status->status = cc_status;
    return nullptr;
  }
  if (var_info->var_info.var() == nullptr) {
    cc_status = absl::InvalidArgumentError(
        "VariableInfo does not track a resource variable.");
    status->status = cc_status;
    return nullptr;
  }