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

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',

output_format: Output format as a string.

  Returns:
    A `tuple` of two strings:
      (standardized_input_format, standardized_output_format).
  """
  # https://github.com/tensorflow/tfjs/issues/1292: Remove the logic for the
  # explicit error message of the deprecated model type name 'tensorflowjs'
  # at version 1.1.0.
  if input_format == 'tensorflowjs':
    raise ValueError(
        '--input_format=tensorflowjs has been deprecated. '
        'Use --input_format=tfjs_layers_model instead.')

  input_format_is_keras = (
      input_format in [common.KERAS_MODEL, common.KERAS_SAVED_MODEL])
  input_format_is_tf = (
      input_format in [common.TF_SAVED_MODEL, common.TF_HUB_MODEL])
  if output_format is None:
    # If no explicit output_format is provided, infer it from input format.
    if input_format_is_keras:
      output_format = common.TFJS_LAYERS_MODEL
    elif input_format_is_tf:
      output_format = common.TFJS_GRAPH_MODEL
    elif input_format == common.TFJS_LAYERS_MODEL:
      output_format = common.KERAS_MODEL
  elif output_format == 'tensorflowjs':
    # https://github.com/tensorflow/tfjs/issues/1292: Remove the logic for the
    # explicit error message of the deprecated model type name 'tensorflowjs'
    # at version 1.1.0.
    if input_format_is_keras:
      raise ValueError(

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):
      detected_input_format = common.TFJS_LAYERS_MODEL

  return detected_input_format, input_path

def _standardize_input_output_formats(input_format, output_format):
  """Standardize input and output formats.

  Args:
    input_format: Input format as a string.
    output_format: Output format as a string.

  Returns:
    A `tuple` of two strings:
      (standardized_input_format, standardized_output_format).
  """
  input_format_is_keras = (
      input_format in [common.KERAS_MODEL, common.KERAS_SAVED_MODEL])
  input_format_is_tf = (
      input_format in [common.TF_SAVED_MODEL,
                       common.TF_FROZEN_MODEL, common.TF_HUB_MODEL])
  if output_format is None:
    # If no explicit output_format is provided, infer it from input format.
    if input_format_is_keras:
      output_format = common.TFJS_LAYERS_MODEL
    elif input_format_is_tf:
      output_format = common.TFJS_GRAPH_MODEL
    elif input_format == common.TFJS_LAYERS_MODEL:
      output_format = common.KERAS_MODEL

  return (input_format, output_format)

input_format = answers[common.INPUT_FORMAT]
  if input_format == common.KERAS_SAVED_MODEL:
    return [{
        'key': 'g', # shortcut key for the option
        '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',
        'name': 'Keras Model (HDF5)',
        'value': common.KERAS_MODEL,
    }, {
        'key': 'l',
        'name': 'TensoFlow.js Layers Model',
        'value': common.TFJS_LAYERS_MODEL,
    }]
  return []

raise ValueError(
        '--input_format=tensorflowjs has been deprecated. '
        'Use --input_format=tfjs_layers_model instead.')

  input_format_is_keras = (
      input_format in [common.KERAS_MODEL, common.KERAS_SAVED_MODEL])
  input_format_is_tf = (
      input_format in [common.TF_SAVED_MODEL, common.TF_HUB_MODEL])
  if output_format is None:
    # If no explicit output_format is provided, infer it from input format.
    if input_format_is_keras:
      output_format = common.TFJS_LAYERS_MODEL
    elif input_format_is_tf:
      output_format = common.TFJS_GRAPH_MODEL
    elif input_format == common.TFJS_LAYERS_MODEL:
      output_format = common.KERAS_MODEL
  elif output_format == 'tensorflowjs':
    # https://github.com/tensorflow/tfjs/issues/1292: Remove the logic for the
    # explicit error message of the deprecated model type name 'tensorflowjs'
    # at version 1.1.0.
    if input_format_is_keras:
      raise ValueError(
          '--output_format=tensorflowjs has been deprecated under '
          '--input_format=%s. Use --output_format=tfjs_layers_model '
          'instead.' % input_format)
    if input_format_is_tf:
      raise ValueError(
          '--output_format=tensorflowjs has been deprecated under '
          '--input_format=%s. Use --output_format=tfjs_graph_model '
          'instead.' % input_format)

  return (input_format, output_format)

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):
    tf_saved_model_conversion_v2.convert_tf_hub_module(
        args.input_path, args.output_path, args.signature_name,
        args.saved_model_tags, skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.KERAS_MODEL):
    dispatch_tensorflowjs_to_keras_h5_conversion(args.input_path,
                                                 args.output_path)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.KERAS_SAVED_MODEL):
    dispatch_tensorflowjs_to_keras_saved_model_conversion(args.input_path,
                                                          args.output_path)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.TFJS_LAYERS_MODEL):
    dispatch_tensorflowjs_to_tensorflowjs_conversion(
        args.input_path, args.output_path,
        quantization_dtype=_parse_quantization_bytes(args.quantization_bytes),
        weight_shard_size_bytes=weight_shard_size_bytes)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
        args.input_path, args.output_path,

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):
    tf_saved_model_conversion_v2.convert_tf_hub_module(
        args.input_path, args.output_path, args.signature_name,
        args.saved_model_tags, skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.KERAS_MODEL):
    dispatch_tensorflowjs_to_keras_h5_conversion(args.input_path,
                                                 args.output_path)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.KERAS_SAVED_MODEL):
    dispatch_tensorflowjs_to_keras_saved_model_conversion(args.input_path,
                                                          args.output_path)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.TFJS_LAYERS_MODEL):
    dispatch_tensorflowjs_to_tensorflowjs_conversion(
        args.input_path, args.output_path,
        quantization_dtype=_parse_quantization_bytes(args.quantization_bytes),
        weight_shard_size_bytes=weight_shard_size_bytes)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
        args.input_path, args.output_path,

elif os.path.isdir(input_path):
    if (any(fname.lower().endswith('saved_model.pb')
            for fname in os.listdir(input_path))):
      detected_input_format = detect_saved_model(input_path)
    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'):
      input_path = os.path.dirname(input_path)
      detected_input_format = detect_saved_model(input_path)
    elif (input_path.endswith('model.json') and
          get_tfjs_model_type(input_path) == common.TFJS_LAYERS_MODEL_FORMAT):
      detected_input_format = common.TFJS_LAYERS_MODEL

  return detected_input_format, input_path