code: def calc_vision_properties(
      raw_data: List, properties_list: List
  ) -> Dict[str, list]:
      """
      Calculate the image properties for a batch of images.
  
      Parameters
      ----------
      raw_data : torch.Tensor
          Batch of images to transform to image properties.
  
      properties_list: List[Dict] , default: None
          A list of properties to calculate.
  
      Returns
      ------
      batch_properties: dict[str, List]
          A dict of property name, property value per sample.
      """
      batch_properties = defaultdict(list)
      for single_property in properties_list:
          property_list = single_property["method"](raw_data)
          batch_properties[single_property["name"]] = property_list
      return batch_properties
  
file_path: deepchecks/deepchecks/vision/utils/vision_properties.py
imports: from enum import Enum
  from collections import defaultdict
  from typing import Any, Dict, List
  from deepchecks.core.errors import DeepchecksValueError
label_desc: Use image node_id 1 for calling a global function with example usage: calc_vision_properties(raw_data, properties_list) and returns: batch_properties