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 labels for the batch in numpy format.
docstr_tok: ["Return","labels","for","the","batch","in","numpy","format","."]
code: def numpy_labels(self) -> List[Union[np.ndarray, int]]:
      """Return labels for the batch in numpy format."""
      required_dim = (
          0 if self._task_type == TaskType.CLASSIFICATION else 2
      )
      return sequence_to_numpy(
          self.original_labels, 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 5 for calling the BatchWrapper obj's underlying member method code with example usage: obj.numpy_labels() and returns: sequence_to_numpy