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 in numpy format.
docstr_tok: ["Return","predictions","for","the","batch","in","numpy","format","."]
code: def numpy_predictions(self) -> List[np.ndarray]:
      """Return predictions for the batch in numpy format."""
      if self._task_type == TaskType.CLASSIFICATION:
          required_dim = 1
      elif self._task_type == TaskType.OBJECT_DETECTION:
          required_dim = 2
      elif self._task_type == TaskType.SEMANTIC_SEGMENTATION:
          required_dim = 3
      else:
          required_dim = None
      return sequence_to_numpy(
          self.original_predictions,
          expected_ndim_per_object=required_dim,
      )
  
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 7 for calling the BatchWrapper obj's underlying member method code with example usage: obj.numpy_predictions() and returns: sequence_to_numpy