std::vector<tensorflow::Output> OutputsFromTFOutputs(TF_Output* tf_outputs,
                                                     int n) {
  std::vector<tensorflow::Output> outputs(n);
  for (int i = 0; i < n; ++i) {
    outputs[i] =
        tensorflow::Output(&tf_outputs[i].oper->node, tf_outputs[i].index);
  }
  return outputs;
}

void TFOutputsFromOutputs(const std::vector<tensorflow::Output>& outputs,

    TF_RETURN_IF_ERROR(
        RunAndMaybeSum(ctx, forward, theta_inputs, f_outputs, use_function));
    AbstractTensorHandlePtr fPlus(f_outputs[0]);

    // Get f(theta - eps):
    theta_inputs[input_index] = thetaMinus.get();
    TF_RETURN_IF_ERROR(
        RunAndMaybeSum(ctx, forward, theta_inputs, f_outputs, use_function));
    AbstractTensorHandlePtr fMinus(f_outputs[0]);

  output_tensors->reserve(noutputs);
  for (int i = 0; i < noutputs; ++i) {
    Node* node = outputs[i].oper ? &outputs[i].oper->node : nullptr;
    int idx = outputs[i].index;
    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing output ", i, " from function '", fn_name,
        "'");
    TF_RETURN_WITH_CONTEXT_IF_ERROR(ValidateNonRefOutput(node, idx),

    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  absl::Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);

    }
    if (TF_GetCode(s) != TF_OK) return;
  }
  std::vector<TFE_TensorHandle*> op_outputs(*num_outputs);
  TFE_Execute(op, op_outputs.data(), num_outputs, s);
  TFE_DeleteOp(op);
  if (TF_GetCode(s) != TF_OK) return;
  std::vector<TFE_TensorHandle*> unwrapped_outputs;
  unwrapped_outputs.reserve(op_outputs.size());
  for (auto* handle : op_outputs) {
    unwrapped_outputs.push_back(handle);
  }

  TF_OutputListSetNumOutputs(mm_outputs, 1, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_ExecuteOperation(fn_op, 1, &input_t, mm_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  ASSERT_EQ(1, TF_OutputListNumOutputs(mm_outputs));
  TF_AbstractTensor* final_result = TF_OutputListGet(mm_outputs, 0);
  TFE_TensorHandle* final =

                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,
                /*targets*/ nullptr, /*ntargets*/ 0,
                /*run_metadata*/ nullptr, status);
  if (VLOG_IS_ON(1) && status->status.ok()) {
    tensorflow::Tensor tensor;

      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, grad_inputs, s_, grad_outputs);
    } else {
      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, nullptr, s_, grad_outputs);
    }
  }

  void BuildErrorGraph(TF_Output* inputs, TF_Output* outputs) {

        committer: TensorFlow Release Automation <******@****.***>
        token: ${{ secrets.JENKINS_TOKEN }}
        base: ${{ github.event.inputs.release_branch }}
        branch: ${{ github.event.inputs.release_branch }}-${{ steps.cherrypick.outputs.SHORTSHA }}
        reviewers: learning-to-play
        body: |

          TAG="quay.io/minio/minio:${{ steps.vars.outputs.sha_short }}" make docker

      - name: multipart uploads test
        run: |
          ${GITHUB_WORKSPACE}/.github/workflows/multipart/migrate.sh "${{ steps.vars.outputs.sha_short }}"

      - name: compress and encrypt
        run: |
          ${GITHUB_WORKSPACE}/.github/workflows/run-mint.sh "compress-encrypt" "minio" "minio123" "${{ steps.vars.outputs.sha_short }}"