code: def _parse_categories(categories):
      if categories is None:
          num_categories = int(torch.randint(1, 11, ()))
      elif isinstance(categories, int):
          num_categories = categories
          categories = [
              f"category{idx}" for idx in range(num_categories)
          ]
      elif isinstance(categories, collections.abc.Sequence) and all(
          isinstance(category, str) for category in categories
      ):
          categories = list(categories)
          num_categories = len(categories)
      else:
          raise pytest.UsageError(
              f"`categories` can either be `None` (default), an integer, or a sequence of strings, "
              f"but got '{categories}' instead."
          )
      return categories, num_categories
  
imports: import collections.abc
  import re
  import PIL.Image
  import pytest
  import torch
  from common_utils import assert_equal, make_bounding_boxes, make_detection_masks, make_image, make_video
  from torchvision.prototype import transforms, tv_tensors
  from torchvision.transforms.v2._utils import check_type, is_pure_tensor
  from torchvision.transforms.v2.functional import clamp_bounding_boxes, InterpolationMode, pil_to_tensor, to_pil_image
  from torchvision.tv_tensors import BoundingBoxes, BoundingBoxFormat, Image, Mask, Video
  import importlib.machinery
  import importlib.util
  from pathlib import Path
label_desc: Use image node_id 1 for calling a global function with example usage: _parse_categories(categories) and returns: categories,  num_categories