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

'type': 'confirm',
          'name': common.STRIP_DEBUG_OPS,
          'message': 'Do you want to strip debug ops? \n'
                     'This will improve model execution performance.',
          'default': True,
          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
                                                (common.TF_SAVED_MODEL,
                                                 common.TF_HUB_MODEL))
      }
  ]
  params = PyInquirer.prompt(questions, format_params, style=prompt_style)

  output_options = [
      {
          'type': 'input',
          'name': common.OUTPUT_PATH,
          'message': 'Which directory do you want to save '
                     'the converted model in?',
          'filter': lambda path: os.path.expanduser(path.strip()),
          'validate': lambda path: len(path) > 0
      }, {
          'type': 'confirm',
          'message': 'The output already directory exists, '
                     'do you want to overwrite it?',
          'name': 'overwrite_output_path',
          'default': False,
          'when': lambda ans: output_path_exists(ans[common.OUTPUT_PATH])
      }
  ]

  while (not common.OUTPUT_PATH in params or
         output_path_exists(params[common.OUTPUT_PATH]) and

'message': 'Do you want to compress the model? '
                     '(this will decrease the model precision.)',
          'choices': [{
              'name': 'No compression (Higher accuracy)',
              'value': None
          }, {
              'name': '2x compression (Accuracy/size trade-off)',
              'value': 2
          }, {
              'name': '4x compression (Smaller size)',
              'value': 1
          }]
      },
      {
          'type': 'input',
          'name': common.WEIGHT_SHARD_SIZE_BYTES,
          'message': 'Please enter shard size (in bytes) of the weight files?',
          'default': str(4 * 1024 * 1024),
          'when': lambda answers: value_in_list(answers, common.OUTPUT_FORMAT,
                                                (common.TFJS_LAYERS_MODEL))
      },
      {
          'type': 'confirm',
          'name': common.SPLIT_WEIGHTS_BY_LAYER,
          'message': 'Do you want to split weights by layers?',
          'default': False,
          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
                                                (common.TFJS_LAYERS_MODEL))
      },
      {
          'type': 'confirm',
          'name': common.SKIP_OP_CHECK,

'"tf_saved_model".')
  parser.add_argument(
      '--%s' % common.QUANTIZATION_BYTES,
      type=int,
      choices=set(quantization.QUANTIZATION_BYTES_TO_DTYPES.keys()),
      help='How many bytes to optionally quantize/compress the weights to. 1- '
      'and 2-byte quantizaton is supported. The default (unquantized) size is '
      '4 bytes.')
  parser.add_argument(
      '--%s' % common.SPLIT_WEIGHTS_BY_LAYER,
      action='store_true',
      help='Applicable to keras input_format only: Whether the weights from '
      'different layers are to be stored in separate weight groups, '
      'corresponding to separate binary weight files. Default: False.')
  parser.add_argument(
      '--%s' % common.VERSION,
      '-v',
      dest='show_version',
      action='store_true',
      help='Show versions of tensorflowjs and its dependencies')
  parser.add_argument(
      '--%s' % common.SKIP_OP_CHECK,
      action='store_true',
      help='Skip op validation for TensorFlow model conversion.')
  parser.add_argument(
      '--%s' % common.STRIP_DEBUG_OPS,
      type=bool,
      default=True,
      help='Strip debug ops (Print, Assert, CheckNumerics) from graph.')
  parser.add_argument(
      '--%s' % common.WEIGHT_SHARD_SIZE_BYTES,
      type=int,

common.TFJS_GRAPH_MODEL))
      },
      {
          'type': 'list',
          'name': common.SIGNATURE_NAME,
          'message': 'What is signature name of the model?',
          'choices': available_signature_names,
          'when': lambda answers: (is_saved_model(answers[common.INPUT_FORMAT])
                                   and
                                   (common.OUTPUT_FORMAT not in format_params
                                    or format_params[common.OUTPUT_FORMAT] ==
                                    common.TFJS_GRAPH_MODEL))
      },
      {
          'type': 'list',
          'name': common.QUANTIZATION_BYTES,
          'message': 'Do you want to compress the model? '
                     '(this will decrease the model precision.)',
          'choices': [{
              'name': 'No compression (Higher accuracy)',
              'value': None
          }, {
              'name': '2x compression (Accuracy/size trade-off)',
              'value': 2
          }, {
              'name': '4x compression (Smaller size)',
              'value': 1
          }]
      },
      {
          'type': 'input',
          'name': common.WEIGHT_SHARD_SIZE_BYTES,

'type': 'confirm',
          'name': common.STRIP_DEBUG_OPS,
          'message': 'Do you want to strip debug ops? \n'
                     'This will improve model execution performance.',
          'default': True,
          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
                                                (common.TF_SAVED_MODEL,
                                                 common.TF_HUB_MODEL))
      }
  ]
  params = PyInquirer.prompt(questions, format_params, style=prompt_style)

  output_options = [
      {
          'type': 'input',
          'name': common.OUTPUT_PATH,
          'message': 'Which directory do you want to save '
                     'the converted model in?',
          'filter': lambda path: update_output_path(path, params),
          'validate': lambda path: len(path) > 0
      },
      {
          'type': 'confirm',
          'message': 'The output already directory exists, '
                     'do you want to overwrite it?',
          'name': 'overwrite_output_path',
          'default': False,
          'when': lambda ans: output_path_exists(ans[common.OUTPUT_PATH])
      }
  ]

  while (common.OUTPUT_PATH not in params or

'message': 'Please enter shard size (in bytes) of the weight files?',
          'default': str(4 * 1024 * 1024),
          'when': lambda answers: value_in_list(answers, common.OUTPUT_FORMAT,
                                                (common.TFJS_LAYERS_MODEL))
      },
      {
          'type': 'confirm',
          'name': common.SPLIT_WEIGHTS_BY_LAYER,
          'message': 'Do you want to split weights by layers?',
          'default': False,
          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
                                                (common.TFJS_LAYERS_MODEL))
      },
      {
          'type': 'confirm',
          'name': common.SKIP_OP_CHECK,
          'message': 'Do you want to skip op validation? \n'
                     'This will allow conversion of unsupported ops, \n'
                     'you can implement them as custom ops in tfjs-converter.',
          'default': False,
          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
                                                (common.TF_SAVED_MODEL,
                                                 common.TF_HUB_MODEL))
      },
      {
          'type': 'confirm',
          'name': common.STRIP_DEBUG_OPS,
          'message': 'Do you want to strip debug ops? \n'
                     'This will improve model execution performance.',
          'default': True,
          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
                                                (common.TF_SAVED_MODEL,

# 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(
          '--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)

format_params = PyInquirer.prompt(formats, input_params, style=prompt_style)
  message = input_path_message(format_params)

  questions = [
      {
          'type': 'input',
          'name': common.INPUT_PATH,
          'message': message,
          'filter': expand_input_path,
          'validate': lambda value: validate_input_path(
              value, format_params[common.INPUT_FORMAT]),
          'when': lambda answers: (not detected_input_format)
      },
      {
          'type': 'list',
          'name': common.SAVED_MODEL_TAGS,
          'choices': available_tags,
          'message': 'What is tags for the saved model?',
          'when': lambda answers: (is_saved_model(answers[common.INPUT_FORMAT])
                                   and
                                   (not common.OUTPUT_FORMAT in format_params
                                    or format_params[common.OUTPUT_FORMAT] ==
                                    common.TFJS_GRAPH_MODEL))
      },
      {
          'type': 'list',
          'name': common.SIGNATURE_NAME,
          'message': 'What is signature name of the model?',
          'choices': available_signature_names,
          'when': lambda answers: (is_saved_model(answers[common.INPUT_FORMAT])
                                   and
                                   (not common.OUTPUT_FORMAT in format_params