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' || '' }}

import com.google.common.collect.Lists;
import com.google.common.primitives.Doubles;
import com.google.common.primitives.Ints;
import java.math.BigInteger;
import java.util.List;
import java.util.stream.DoubleStream;
import org.jspecify.annotations.NullUnmarked;

/**
 * Inputs, expected outputs, and helper methods for tests of {@link StatsAccumulator}, {@link
 * Stats}, {@link PairedStatsAccumulator}, and {@link PairedStats}.

    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,

                    inputs = new String[inputStrings.size()];
                    for (int i = 0; i < inputs.length; i++) {
                        inputs[i] = unescape(inputStrings.get(i)).trim();
                    }

                    if (inputs.length > 0) {
                        id++;
                        final SynonymItem item = new SynonymItem(id, inputs, inputs);

	for i = range uint(8) {
		fi := FileInfo{
			TransitionTier:      inputs[0].tier,
			TransitionedObjName: inputs[0].remoteObjName,
			TransitionVersionID: inputs[0].remoteVersionID,
			TransitionStatus:    inputs[0].status,
		}
		ofi := fi
		if i&(1<<0) != 0 {
			ofi.TransitionTier = inputs[1].tier
		}
		if i&(1<<1) != 0 {
			ofi.TransitionedObjName = inputs[1].remoteObjName
		}
		if i&(1<<2) != 0 {

        with:
          limit-access-to-actor: true
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      - name: Notify Translations
        run: uv run ./scripts/notify_translations.py
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          NUMBER: ${{ github.event.inputs.number || null }}

  // 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_)));

  // watched inputs.
  void Watch(const AbstractTensorHandle*);
  // Records an operation with given inputs and outputs
  // on the tape and marks all its outputs as watched if at
  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,

  @SafeVarargs
  public static <T extends @Nullable Object> Iterator<T> concat(Iterator<? extends T>... inputs) {
    return concatNoDefensiveCopy(Arrays.copyOf(inputs, inputs.length));
  }

  /**
   * Combines multiple iterators into a single iterator. The returned iterator iterates across the
   * elements of each iterator in {@code inputs}. The input iterators are not polled until
   * necessary.
   *