How to use the tensorflowjs.converters.common.KERAS_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',

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',

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 or \
      input_format == common.KERAS_SAVED_MODEL

'name': 'Tensorflow.js Graph Model',
        'value': common.TFJS_GRAPH_MODEL,
    }, {
        'key': 'l',
        'name': 'TensoFlow.js Layers Model',
        'value': common.TFJS_LAYERS_MODEL,
    }]
  if input_format == common.TFJS_LAYERS_MODEL:
    return [{
        'key': 'k', # shortcut key for the option
        'name': 'Keras Model',
        'value': common.KERAS_MODEL,
    }, {
        'key': 's',
        'name': 'Keras Saved Model',
        'value': common.KERAS_SAVED_MODEL,
    }]
  return []

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

# 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(
        args.input_path, output_dir=args.output_path,
        quantization_dtype=quantization_dtype,
        skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.KERAS_SAVED_MODEL and
        output_format == common.TFJS_LAYERS_MODEL):
    dispatch_keras_saved_model_to_tensorflowjs_conversion(
        args.input_path, args.output_path,
        quantization_dtype=quantization_dtype,
        split_weights_by_layer=args.split_weights_by_layer)
  elif (input_format == common.TF_SAVED_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    tf_saved_model_conversion_v2.convert_tf_saved_model(
        args.input_path, args.output_path,
        signature_def=args.signature_name,
        saved_model_tags=args.saved_model_tags,
        quantization_dtype=quantization_dtype,
        skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.TF_HUB_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):

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, '
      ' or TF-Hub module is expected.')

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

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,