"Unable to find the placeholder node as input to enqueue in the TF "
        "graph.");
    return;
  }

  VLOG(1) << "Running the enqueue op";
  TF_Output input{placeholder_op, 0};
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ &input, /*input_values*/ &tensor, /*ninputs*/ 1,
                // output related parameters

  parallel_inputs.reserve(inputs.size());
  implicitly_broadcast_tensors.reserve(inputs.size());  // not tight
  for (const auto& input : inputs) {
    if (absl::holds_alternative<TFE_TensorHandle*>(input)) {
      if (operation_name == std::string("_EagerConst")) {
        // Non-parallel tensors from _EagerConst/tf.constant are implicitly
        // broadcast, i.e. set as the input to each parallel operation. This

		}
	}

	var inputs []string

	// Parse parameters
	switch {
	case *stdin:
		// Parse 'mc support inspect --json' output
		input := struct {
			File string `json:"file"`
			Key  string `json:"key"`
		}{}
		got, err := io.ReadAll(os.Stdin)
		if err != nil {
			fatalErr(err)
		}
		fatalErr(json.Unmarshal(got, &input))
		inputs = []string{input.File}
		*keyHex = input.Key

      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) {

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
                     const char* name = "split3");

bool IsPlaceholder(const tensorflow::NodeDef& node_def);

// GetSubSys - extracts subssystem info from given config string
func GetSubSys(s string) (subSys string, inputs []string, tgt string, e error) {
	tgt = Default
	if len(s) == 0 {
		return subSys, inputs, tgt, Errorf("input arguments cannot be empty")
	}
	inputs = strings.SplitN(s, KvSpaceSeparator, 2)

	subSystemValue := strings.SplitN(inputs[0], SubSystemSeparator, 2)
	subSys = subSystemValue[0]
	if !SubSystems.Contains(subSys) {

  for (auto input : inputs) {
    forward_op_->inputs.push_back(input);
  }
  return absl::OkStatus();
}

absl::Status SetAttrString(AbstractOperation* op_, const char* attr_name,
                           const char* data, size_t length,
                           ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, StringPiece(data, length));

jobs:
  cherrypick:
    name: Cherrypick to ${{ github.event.inputs.release_branch}} - ${{ github.event.inputs.git_commit }}
    runs-on: ubuntu-latest
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    steps:
    - name: Checkout code
      uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0
      with:
        ref: ${{ github.event.inputs.release_branch }}

//
// Does not use the operation's definition to determine how many inputs should
// be attached. It is intended for use with TFE_OpGetFlatInput to inspect an
// already-finalized operation.
//
// Note that TFE_OpGetFlatInputCount and TFE_OpGetFlatInput operate on a flat
// sequence of inputs, unlike TFE_OpGetInputLength (for getting the length of a

public class StopwordsItem extends DictionaryItem {
    private final String input;

    private String newInput;

    public StopwordsItem(final long id, final String input) {
        this.id = id;
        this.input = input;

        if (id == 0) {
            // create
            newInput = input;
        }
    }

    public String getNewInput() {
        return newInput;
    }