class_docstr: Represents dataset batch returned by the dataloader during iteration.
class_docstr_tok: ["Represents","dataset","batch","returned","by","the","dataloader","during","iteration","."]
code: def _get_relevant_data_for_properties(
      self, input_type: PropertiesInputType
  ):
      result = []
      if input_type == PropertiesInputType.PARTIAL_IMAGES:
          for img, bboxes_in_img in zip(
              self.numpy_images, self.numpy_labels
          ):
              if bboxes_in_img is None:
                  continue
              result = result + [
                  crop_image(img, *bbox[1:]) for bbox in bboxes_in_img
              ]
      elif input_type == PropertiesInputType.IMAGES:
          result = self.numpy_images
      elif input_type == PropertiesInputType.LABELS:
          result = self.numpy_labels
      elif input_type == PropertiesInputType.PREDICTIONS:
          result = self.numpy_predictions
      return result
  
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 2 for calling the BatchWrapper obj's underlying member method code with example usage: obj._get_relevant_data_for_properties(input_type) and returns: result