* @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;
        }
    }

    SEQUENTIAL {
      @Override
      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;
      }
    },

     * @param id the unique identifier for this mapping item
     * @param inputs array of input character sequences that will be mapped to the output
     * @param output the output character sequence that inputs will be mapped to
     */
    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", " ");

}

window.addEventListener("load", function () {
  addLinks();
  console.log("hacking gopher pop-up loaded.");
  document.getElementById("inputbox").focus();
});

window.addEventListener("submit", function () {
  console.log("submitting form");
  var box = document.getElementById("inputbox");
  box.focus();

  var t = box.value;
  if (t == "") {
    return false;
  }

  var success = function(url) {

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 }}

    SEQUENTIAL {
      @Override
      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;
      }
    },

                         absl::Span<AbstractTensorHandle*> outputs) {
  return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0],
                     /*transpose_a=*/false,
                     /*transpose_b=*/false, "MatMul");
}

absl::Status MulModel(AbstractContext* ctx,
                      absl::Span<AbstractTensorHandle* const> inputs,

      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' || '' }}

    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,