code: def filter_boxes_by_class(
      boxes: np.ndarray, class_id: Iterable[int]
  ) -> np.ndarray:
      """Select boxes for a given set of classes.
  
      Args:
       boxes: a numpy array representing the bounding boxes in the following format
         [LEFT, TOP, RIGHT, BOTTOM, CLASS, CONFIDENCE(Optional)]
       class_id: id of target num_classes
  
      Returns:
       filtered_boxes: the filtered bounding boxes
      """
      if boxes.ndim != 2 or boxes.shape[1] <= CLASS:
          raise ValueError(
              f"Input boxes list should be a 2d array of shape "
              f"(<batch_size>, {CLASS}+)"
          )
      if isinstance(class_id, int):
          class_id = [class_id]
      return boxes[np.isin(boxes[:, CLASS], class_id), :]
  
imports: from typing import Iterable, Tuple
  import warnings
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: filter_boxes_by_class(boxes, class_id) and returns: boxes