for (T input : inputs) {
          collector.accumulator().accept(accum, input);
        }
        return accum;
      }
    },
    /** Get one accumulator for each element and merge the accumulators left-to-right. */
    MERGE_LEFT_ASSOCIATIVE {
      @Override
      final <T extends @Nullable Object, A extends @Nullable Object, R extends @Nullable Object>
          A result(Collector<T, A, R> collector, Iterable<T> inputs) {

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build inputs and outputs.
  TF_AbstractTensor* inputs[2] = {at, at};
  TF_OutputList* o = TF_NewOutputList();
  TF_OutputListSetNumOutputs(o, 1, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Execute.
  TF_ExecuteOperation(op, 2, inputs, o, status.get());

    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  input_tensors->reserve(ninputs);
  for (int i = 0; i < ninputs; ++i) {
    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing input ", i, " into function '", fn_name,
        "'");

public class SynonymItem extends DictionaryItem {
    private final String[] inputs;

    private final String[] outputs;

    private String[] newInputs;

    private String[] newOutputs;

    public SynonymItem(final long id, final String[] inputs, final String[] outputs) {
        this.id = id;
        this.inputs = inputs;
        this.outputs = outputs;

        if (id == 0) {
            // create

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@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
      with:
        ref: ${{ github.event.inputs.release_branch }}

  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));

//          automatic skipping of dummy input operations is performed.
//  ninputs - number of elements in `inputs` array
//  inputs - array of TF_Outputs that specify the inputs to the function.
//           If `ninputs` is zero (the function takes no inputs), `inputs`
//           can be null. The names used for function inputs are normalized

  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

    private final String[] inputs;

    private final String output;

    private String[] newInputs;

    private String newOutput;

    public CharMappingItem(final long id, final String[] inputs, final String output) {
        this.id = id;
        this.inputs = inputs;
        this.output = output == null ? null : output.replace("\n", " ");
        Arrays.sort(inputs);

        if (id == 0) {

  // The first one will the function's output
  std::vector<TF_Output> outputs;

  // Add while loop to func_graph_
  {
    // The inputs to the while loop
    std::vector<TF_Output> inputs = {{feed1, 0}, {feed2, 0}};
    std::unique_ptr<TF_WhileParams> params(new TF_WhileParams(
        TF_NewWhile(func_graph_, &inputs[0], inputs.size(), s_)));