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,

//  noutputs - number of elements in `outputs` array
//  outputs - array of TF_Outputs that specify the outputs of the function.
//            If `noutputs` is zero (the function returns no outputs), `outputs`
//            can be null. `outputs` can contain the same tensor more than once.
//  output_names - The names of the function's outputs. `output_names` array

  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),

                // 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) {

        this.id = id;
        this.inputs = inputs;
        this.outputs = outputs;

        if (id == 0) {
            // create
            newInputs = inputs;
            newOutputs = outputs;
        }
    }

    /**
     * Gets the new input words.
     *
     * @return The new input words.
     */
    public String[] getNewInputs() {

          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 }}"

        }
    }

    /**
     * Outputs INFO information.
     *
     * @param message
     *            Message
     */
    public void info(final Object message) {
        if (isInfoEnabled()) {
            log.info(toString(message));
        }
    }

    /**
     * Outputs WARN information.
     *
     * @param message
     *            Message

env:
  UV_SYSTEM_PYTHON: 1

jobs:
  changes:
    runs-on: ubuntu-latest
    # Required permissions
    permissions:
      pull-requests: read
    # Set job outputs to values from filter step
    outputs:
      docs: ${{ steps.filter.outputs.docs }}
    steps:
    - uses: actions/checkout@v5
    # For pull requests it's not necessary to checkout the code but for the main branch it is
    - uses: dorny/paths-filter@v3

        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: |