from tensorflow.python.tools import module_util as _module_util
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

        raise typer.Abort()
    typer.echo("Valid README ✅")


@app.command()
def build_all() -> None:
    """
    Build mkdocs site for en, and then build each language inside, end result is located
    at directory ./site/ with each language inside.
    """
    update_languages()
    shutil.rmtree(site_path, ignore_errors=True)
    langs = [lang.name for lang in get_lang_paths() if lang.is_dir()]

  parser = argparse.ArgumentParser(
      description='Extracts ResultStore links from a build log.\n'
                  'These can be then printed out, and/or output into a '
                  'JUnit-based XML file inside a specified directory.')

  parser.add_argument('build_log',
                      help='Path to a build log.')
  parser.add_argument('--xml-out-path',
                      required=False,

  paths = glob.glob('**/third_party/gpus/find_cuda_config.py', recursive=True)
  if not paths:
    raise FileNotFoundError(
        "Can't find 'find_cuda_config.py' script inside working directory")
  proc = subprocess.Popen(
      [environ_cp['PYTHON_BIN_PATH'], paths[0]] + cuda_libraries,
      stdout=subprocess.PIPE,
      env=maybe_encode_env(environ_cp))

  if proc.wait():