class_docstr: Represents dataset batch returned by the dataloader during iteration.
class_docstr_tok: ["Represents","dataset","batch","returned","by","the","dataloader","during","iteration","."]
docstr: Return predictions for the batch, formatted in deepchecks format.
docstr_tok: ["Return","predictions","for","the","batch",",","formatted","in","deepchecks","format","."]
code: def original_predictions(self):
      """Return predictions for the batch, formatted in deepchecks format."""
      if self._predictions is None:
          self._predictions = self._batch.get("predictions")
      return self._predictions
  
imports: from typing import Dict, List, Optional, Union
  import numpy
  from deepchecks.core.errors import DeepchecksProcessError
  from deepchecks.vision.utils.image_functions import crop_image
  from deepchecks.vision.utils.image_properties import calc_default_image_properties, default_image_properties
  from deepchecks.vision.utils.vision_properties import PropertiesInputType, calc_vision_properties, validate_properties
  from deepchecks.vision.vision_data.utils import BatchOutputFormat, TaskType, sequence_to_numpy
label_desc: Use image node_id 6 for calling the BatchWrapper obj's underlying member method code with example usage: obj.original_predictions() and returns: self