How to use the tensorflowjs.converters.common.TF_SAVED_MODEL function in tensorflowjs

def input_formats(detected_format):
  formats = [{
      'key': 'k',
      'name': input_format_string('Keras (HDF5)', common.KERAS_MODEL,
                                  detected_format),
      'value': common.KERAS_MODEL
  }, {
      'key': 'e',
      'name': input_format_string('Tensorflow Keras Saved Model',
                                  common.KERAS_SAVED_MODEL,
                                  detected_format),
      'value': common.KERAS_SAVED_MODEL,
  }, {
      'key': 's',
      'name': input_format_string('Tensorflow Saved Model',
                                  common.TF_SAVED_MODEL,
                                  detected_format),
      'value': common.TF_SAVED_MODEL,
  }, {
      'key': 'h',
      'name': input_format_string('TFHub Module',
                                  common.TF_HUB_MODEL,
                                  detected_format),
      'value': common.TF_HUB_MODEL,
  }, {
      'key': 'l',
      'name': input_format_string('TensoFlow.js Layers Model',
                                  common.TFJS_LAYERS_MODEL,
                                  detected_format),
      'value': common.TFJS_LAYERS_MODEL,
  }]
  formats.sort(key=lambda x: x['value'] != detected_format)

def detect_saved_model(input_path):
  if os.path.exists(os.path.join(input_path, 'assets', 'saved_model.json')):
    return common.KERAS_SAVED_MODEL
  saved_model = loader_impl.parse_saved_model(input_path)
  graph_def = saved_model.meta_graphs[0].object_graph_def
  if graph_def.nodes:
    if 'tf_keras' in graph_def.nodes[0].user_object.identifier:
      return common.KERAS_SAVED_MODEL
  return common.TF_SAVED_MODEL

nargs='?',
      type=str,
      help='Path to the input file or directory. For input format "keras", '
      'an HDF5 (.h5) file is expected. For input format "tensorflow", '
      'a SavedModel directory, frozen model file, '
      'or TF-Hub module is expected.')
  parser.add_argument(
      common.OUTPUT_PATH,
      nargs='?',
      type=str,
      help='Path for all output artifacts.')
  parser.add_argument(
      '--%s' % common.INPUT_FORMAT,
      type=str,
      required=False,
      default=common.TF_SAVED_MODEL,
      choices=set([common.KERAS_MODEL, common.KERAS_SAVED_MODEL,
                   common.TF_SAVED_MODEL, common.TF_HUB_MODEL,
                   common.TFJS_LAYERS_MODEL, common.TF_FROZEN_MODEL]),
      help='Input format. '
      'For "keras", the input path can be one of the two following formats:\n'
      '  - A topology+weights combined HDF5 (e.g., generated with'
      '    `keras.model.save_model()` method).\n'
      '  - A weights-only HDF5 (e.g., generated with Keras Model\'s '
      '    `save_weights()` method). \n'
      'For "keras_saved_model", the input_path must point to a subfolder '
      'under the saved model folder that is passed as the argument '
      'to tf.contrib.save_model.save_keras_model(). '
      'The subfolder is generated automatically by tensorflow when '
      'saving keras model in the SavedModel format. It is usually named '
      'as a Unix epoch time (e.g., 1542212752).\n'
      'For "tf" formats, a SavedModel, frozen model, '

weight_shard_size_bytes = args.weight_shard_size_bytes

  if args.input_path is None:
    raise ValueError(
        'Error: The input_path argument must be set. '
        'Run with --help flag for usage information.')

  input_format, output_format = _standardize_input_output_formats(
      args.input_format, args.output_format)

  quantization_dtype = (
      quantization.QUANTIZATION_BYTES_TO_DTYPES[args.quantization_bytes]
      if args.quantization_bytes else None)

  if (args.signature_name and input_format not in
      (common.TF_SAVED_MODEL, common.TF_HUB_MODEL)):
    raise ValueError(
        'The --signature_name flag is applicable only to "tf_saved_model" and '
        '"tf_hub" input format, but the current input format is '
        '"%s".' % input_format)

  # TODO(cais, piyu): More conversion logics can be added as additional
  #   branches below.
  if (input_format == common.KERAS_MODEL and
      output_format == common.TFJS_LAYERS_MODEL):
    dispatch_keras_h5_to_tfjs_layers_model_conversion(
        args.input_path, output_dir=args.output_path,
        quantization_dtype=quantization_dtype,
        split_weights_by_layer=args.split_weights_by_layer)
  elif (input_format == common.KERAS_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    dispatch_keras_h5_to_tfjs_graph_model_conversion(

def detect_input_format(input_path):
  """Determine the input format from model's input path or file.
  Args:
    input_path: string of the input model path
  returns:
    string: detected input format
    string: normalized input path
  """
  input_path = input_path.strip()
  detected_input_format = None
  if re.match(TFHUB_VALID_URL_REGEX, input_path):
    detected_input_format = common.TF_HUB_MODEL
  elif os.path.isdir(input_path):
    if (any(fname.lower().endswith('saved_model.pb')
            for fname in os.listdir(input_path))):
      detected_input_format = common.TF_SAVED_MODEL
    else:
      for fname in os.listdir(input_path):
        fname = fname.lower()
        if fname.endswith('model.json'):
          filename = os.path.join(input_path, fname)
          if get_tfjs_model_type(filename) == common.TFJS_LAYERS_MODEL_FORMAT:
            input_path = os.path.join(input_path, fname)
            detected_input_format = common.TFJS_LAYERS_MODEL
            break
  elif os.path.isfile(input_path):
    if h5py.is_hdf5(input_path):
      detected_input_format = common.KERAS_MODEL
    elif input_path.endswith('saved_model.pb'):
      detected_input_format = common.TF_SAVED_MODEL
    elif (input_path.endswith('model.json') and
          get_tfjs_model_type(input_path) == common.TFJS_LAYERS_MODEL_FORMAT):

'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
                                                (common.TF_SAVED_MODEL,
                                                 common.TF_HUB_MODEL))
      }

'Error: The input_path argument must be set. '
        'Run with --help flag for usage information.')

  input_format, output_format = _standardize_input_output_formats(
      args.input_format, args.output_format)

  quantization_dtype = (
      quantization.QUANTIZATION_BYTES_TO_DTYPES[args.quantization_bytes]
      if args.quantization_bytes else None)

  if (not args.output_node_names and input_format == common.TF_FROZEN_MODEL):
    raise ValueError(
        'The --output_node_names flag is required for "tf_frozen_model"')

  if (args.signature_name and input_format not in
      (common.TF_SAVED_MODEL, common.TF_HUB_MODEL)):
    raise ValueError(
        'The --signature_name flag is applicable only to "tf_saved_model" and '
        '"tf_hub" input format, but the current input format is '
        '"%s".' % input_format)

  # TODO(cais, piyu): More conversion logics can be added as additional
  #   branches below.
  if (input_format == common.KERAS_MODEL and
      output_format == common.TFJS_LAYERS_MODEL):
    dispatch_keras_h5_to_tfjs_layers_model_conversion(
        args.input_path, output_dir=args.output_path,
        quantization_dtype=quantization_dtype,
        split_weights_by_layer=args.split_weights_by_layer)
  elif (input_format == common.KERAS_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    dispatch_keras_h5_to_tfjs_graph_model_conversion(

'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
                                                (common.TF_SAVED_MODEL,
                                                 common.TF_HUB_MODEL))
      },

def is_saved_model(input_format):
  """Check if the input path contains saved model.
  Args:
    input_format: input model format.
  Returns:
    bool: whether this is for a saved model conversion.
  """
  return input_format == common.TF_SAVED_MODEL

def input_formats(detected_format):
  formats = [{
      'key': 'k',
      'name': input_format_string('Keras (HDF5)', common.KERAS_MODEL,
                                  detected_format),
      'value': common.KERAS_MODEL
  }, {
      'key': 'e',
      'name': input_format_string('Tensorflow Keras Saved Model',
                                  common.KERAS_SAVED_MODEL,
                                  detected_format),
      'value': common.KERAS_SAVED_MODEL,
  }, {
      'key': 's',
      'name': input_format_string('Tensorflow Saved Model',
                                  common.TF_SAVED_MODEL,
                                  detected_format),
      'value': common.TF_SAVED_MODEL,
  }, {
      'key': 'h',
      'name': input_format_string('TFHub Module',
                                  common.TF_HUB_MODEL,
                                  detected_format),
      'value': common.TF_HUB_MODEL,
  }, {
      'key': 'l',
      'name': input_format_string('TensoFlow.js Layers Model',
                                  common.TFJS_LAYERS_MODEL,
                                  detected_format),
      'value': common.TFJS_LAYERS_MODEL,
  }]
  formats.sort(key=lambda x: x['value'] != detected_format)