code: def crop_orientation_predictor(
      arch: str = "mobilenet_v3_small_orientation",
      pretrained: bool = False,
      **kwargs: Any,
  ) -> CropOrientationPredictor:
      """Orientation classification architecture.
  
      >>> import numpy as np
      >>> from doctr.models import crop_orientation_predictor
      >>> model = crop_orientation_predictor(arch='classif_mobilenet_v3_small', pretrained=True)
      >>> input_crop = (255 * np.random.rand(600, 800, 3)).astype(np.uint8)
      >>> out = model([input_crop])
  
      Args:
      ----
          arch: name of the architecture to use (e.g. 'mobilenet_v3_small')
          pretrained: If True, returns a model pre-trained on our recognition crops dataset
          **kwargs: keyword arguments to be passed to the CropOrientationPredictor
  
      Returns:
      -------
          CropOrientationPredictor
      """
      return _crop_orientation_predictor(arch, pretrained, **kwargs)
  
imports: from typing import Any, List
  from doctr.file_utils import is_tf_available
  from ..None import classification
  from ..preprocessor import PreProcessor
  from .predictor import CropOrientationPredictor
label_desc: Use image node_id 2 for calling a global function with example usage: crop_orientation_predictor(arch, pretrained) and returns: _crop_orientation_predictor