code: def recognition_predictor(
      arch: Any = "crnn_vgg16_bn",
      pretrained: bool = False,
      **kwargs: Any,
  ) -> RecognitionPredictor:
      """Text recognition architecture.
  
      Example::
          >>> import numpy as np
          >>> from doctr.models import recognition_predictor
          >>> model = recognition_predictor(pretrained=True)
          >>> input_page = (255 * np.random.rand(32, 128, 3)).astype(np.uint8)
          >>> out = model([input_page])
  
      Args:
      ----
          arch: name of the architecture or model itself to use (e.g. 'crnn_vgg16_bn')
          pretrained: If True, returns a model pre-trained on our text recognition dataset
          **kwargs: optional parameters to be passed to the architecture
  
      Returns:
      -------
          Recognition predictor
      """
      return _predictor(arch, pretrained, **kwargs)
  
imports: from typing import Any, List
  from doctr.file_utils import is_tf_available
  from doctr.models.preprocessor import PreProcessor
  from ..None import recognition
  from .predictor import RecognitionPredictor
label_desc: Use image node_id 2 for calling a global function with example usage: recognition_predictor(arch, pretrained) and returns: _predictor