automatically on GitHub.

If you want to contribute, start working through the TensorFlow codebase,
navigate to the
[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.

    *   Added top-k to precision and recall to keras metrics.
    *   Add a ragged size op and register it to the op dispatcher
    *   Transitive dependencies on :`pooling_ops` were removed. Some users may
        need to add explicit dependencies on :`pooling_ops` if they reference
        the operators from that library.
    *   Add `CompositeTensor` base class.

        "//tensorflow/lite/toco:toco_flags_proto_cc",
        "//tensorflow/lite/toco:toco_graphviz_dump_options",
        "//tensorflow/lite/toco:toco_port",
        "//tensorflow/lite/toco:toco_tooling",
        "//tensorflow/lite/toco:tooling_util",
        "//tensorflow/lite/toco:types_proto_cc",
        "//tensorflow/lite:framework",
        "//tensorflow/lite:shared_library",
        "//tensorflow/lite:stateful_error_reporter",

          f'Bazel invocations.\n'
          f'ResultStore contains individual representations of each target '
          f'that were run/built during the invocation.\n'
          f'These results are generally easier to read than looking through '
          f'the entire build log:\n')
  i = 1
  for url, invocation_results in result_store_dict.items():
    line_str = f'Invocation #{i} ({invocation_results["status"]}):\n'

      interpreted as a boolean indicator, assume that the user has made a
      scripting error, and will continue to provide invalid input.
      Raise the error to avoid infinitely looping.
  """
  if not question:
    question = 'Do you wish to build TensorFlow with {} support?'.format(
        query_item)
  if not yes_reply: