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 length of batch.
docstr_tok: ["Return","length","of","batch","."]
code: def __len__(self):
      """Return length of batch."""
      data = (
          self.numpy_images
          if self.numpy_images is not None
          else self.numpy_predictions
          if self.numpy_predictions is not None
          else self.numpy_labels
          if self.numpy_labels is not None
          else self.numpy_embeddings
          if self.numpy_embeddings is not None
          else self.numpy_additional_data
      )
      return len(data)
  
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 16 for calling the BatchWrapper obj's underlying member method code with example usage: obj.__len__() and returns: len