inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  absl::Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);

//  noutputs - number of elements in `outputs` array
//  outputs - array of TF_Outputs that specify the outputs of the function.
//            If `noutputs` is zero (the function returns no outputs), `outputs`
//            can be null. `outputs` can contain the same tensor more than once.
//  output_names - The names of the function's outputs. `output_names` array

env:
  UV_SYSTEM_PYTHON: 1

jobs:
  changes:
    runs-on: ubuntu-latest
    # Required permissions
    permissions:
      pull-requests: read
    # Set job outputs to values from filter step
    outputs:
      docs: ${{ steps.filter.outputs.docs }}
    steps:
    - uses: actions/checkout@v4
    # For pull requests it's not necessary to checkout the code but for the main branch it is
    - uses: dorny/paths-filter@v3

  auto outputs = parallel_device.Execute(
      context.get(), {reduced_values.get(), run_collective.get()},
      "AssertAndCollective", TFE_OpGetAttrs(call_op.get()),
      /*expected_max_outputs=*/1, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  ASSERT_EQ(outputs->size(), 1);
  ParallelTensor* parallel_result = (*outputs)[0].get();

                                    <label for="outputs" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_synonym_target"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="outputs"/>
                                        <la:textarea styleId="outputs" property="outputs" rows="5"

                         const std::vector<IOSpec>& outputs) {
    const OpDef& signature = fdef.signature();
    ASSERT_EQ(outputs.size(), signature.output_arg_size());
    for (int i = 0; i < outputs.size(); ++i) {
      const OpDef::ArgDef& arg = signature.output_arg(i);
      const IOSpec& out = outputs[i];
      if (out.second != DT_INVALID) {
        ASSERT_EQ(arg.type(), out.second)

      with:
        output: ' '
    - name: Report list of changed files
      run: |
        echo Changed files: ${{ steps.get_file_changes.outputs.files }}
    - name: Set up Python 3.9
      uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
      with:
        python-version: "3.9"
    - name: Install Python dependencies
      run: |

        committer: TensorFlow Release Automation <******@****.***>
        token: ${{ secrets.JENKINS_TOKEN }}
        base: ${{ github.event.inputs.release_branch }}
        branch: ${{ github.event.inputs.release_branch }}-${{ steps.cherrypick.outputs.SHORTSHA }}
        reviewers: learning-to-play
        body: |

  for (int i = 0; i < noutputs; ++i) {
    Node* node = outputs[i].oper ? &outputs[i].oper->node : nullptr;
    int idx = outputs[i].index;
    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing output ", i, " from function '", fn_name,
        "'");
    TF_RETURN_WITH_CONTEXT_IF_ERROR(ValidateNonRefOutput(node, idx),

  outputs.dir("$buildDir/generated/sources/idnaMappingTable")
  mainClass.set("okhttp3.internal.idn.GenerateIdnaMappingTableCode")
  args("$buildDir/generated/sources/idnaMappingTable")
  classpath = generateIdnaMappingTableConfiguration
}

kotlin {
  sourceSets {
    getByName("main") {
      kotlin.srcDir(copyKotlinTemplates.get().outputs)
      kotlin.srcDir(generateIdnaMappingTable.outputs)