] = fastapi_specific_annotations[-1]
        else:
            fastapi_annotation = None
        if isinstance(fastapi_annotation, FieldInfo):
            # Copy `field_info` because we mutate `field_info.default` below.
            field_info = copy_field_info(
                field_info=fastapi_annotation, annotation=use_annotation
            )
            assert field_info.default is Undefined or field_info.default is Required, (

    else_: Optional["SchemaOrBool"] = Field(default=None, alias="else")
    dependentSchemas: Optional[Dict[str, "SchemaOrBool"]] = None
    prefixItems: Optional[List["SchemaOrBool"]] = None
    # TODO: uncomment and remove below when deprecating Pydantic v1
    # It generales a list of schemas for tuples, before prefixItems was available
    # items: Optional["SchemaOrBool"] = None
    items: Optional[Union["SchemaOrBool", List["SchemaOrBool"]]] = None

        for name in cuda_env_names:
          if name in environ_cp:
            write_action_env_to_bazelrc(name, environ_cp[name])
        break

      # Restore settings changed below if CUDA config could not be validated.
      environ_cp = dict(environ_save)

      set_tf_cuda_version(environ_cp)
      set_tf_cudnn_version(environ_cp)
      if is_windows():

        sys.exit(0)
    use_pr: Union[PullRequest, None] = None
    for pr in repo.get_pulls():
        if pr.head.sha == event.workflow_run.head_commit.id:
            use_pr = pr
            break
    if not use_pr:
        logging.error(f"No PR found for hash: {event.workflow_run.head_commit.id}")
        sys.exit(0)
    github_headers = {
        "Authorization": f"token {settings.input_token.get_secret_value()}"

TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually
only a placeholder to enable test cases to run. The TensorFlow build replaces

        self.dependencies = list(dependencies or [])
        self.description = description or inspect.cleandoc(self.endpoint.__doc__ or "")
        # if a "form feed" character (page break) is found in the description text,
        # truncate description text to the content preceding the first "form feed"
        self.description = self.description.split("\f")[0].strip()
        response_fields = {}

                for label in pr.labels.nodes:
                    if label.name == "lang-all":
                        translation_reviewers[review.author.login] += 1
                        break
        for reviewer in pr_reviewers:
            reviewers[reviewer] += 1
        if pr.state == "MERGED" and pr.author:
            contributors[pr.author.login] += 1
    return ContributorsResults(

        if verbose:
          print(f'Found failed invocation: {url.rsplit("/")[-1]}\n'
                f'Log fragment:\n'
                f'```\n{log_fragment}\n```\n'
                f'{"=" * 140}')
        break
      k -= 1

    # A low-effort attempt to find the bazel command that triggered the
    # invocation.
    bazel_comm_min_line_i = (previous_end_line if previous_end_line is not None