*/
    public String[] getNewInputs() {
        return newInputs == null ? null : newInputs.clone();
    }

    /**
     * Sets the array of new input character sequences for update operations.
     *
     * @param newInputs array of new input sequences to set
     */
    public void setNewInputs(final String[] newInputs) {
        this.newInputs = newInputs;
    }

    /**

     * @param inputs  The input words.
     * @param outputs The output synonyms.
     */
    public SynonymItem(final long id, final String[] inputs, final String[] outputs) {
        this.id = id;
        this.inputs = inputs;
        this.outputs = outputs;

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

     * @return the input value
     */
    public String getInput() {
        return input;
    }

    /**
     * Gets the input value or empty string if null.
     * @return the input value or empty string
     */
    public String getInputValue() {
        if (input == null) {
            return StringUtil.EMPTY;
        }
        return input;
    }

    /**

    private final String input;

    /** The new stopword, if updated. */
    private String newInput;

    /**
     * Constructs a new stopword item.
     *
     * @param id    The unique identifier of the item.
     * @param input The stopword.
     */
    public StopwordsItem(final long id, final String input) {
        this.id = id;
        this.input = input;

        if (id == 0) {

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);

          A result(Collector<T, A, R> collector, Iterable<T> inputs) {
        List<A> stack = new ArrayList<>();
        for (T input : inputs) {
          A newAccum = collector.supplier().get();
          collector.accumulator().accept(newAccum, input);
          push(stack, newAccum);
        }
        push(stack, collector.supplier().get());

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@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
      with:
        ref: ${{ github.event.inputs.release_branch }}

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

      run:
        shell: bash
    container: ${{ (contains(inputs.runner, 'linux-x86') && 'us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest') ||
                   (contains(inputs.runner, 'linux-arm64') && 'us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest') ||
                   (contains(inputs.runner, 'windows-x86') && null) }}
    timeout-minutes: 300
    steps:

          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          LANGUAGE: ${{ matrix.lang }}
          EN_PATH: ${{ github.event.inputs.en_path }}
          COMMAND: ${{ matrix.command }}
          COMMIT_IN_PLACE: ${{ github.event.inputs.commit_in_place == 'true' && 'true' || '' }}