code: def test_call(self, dims):
      sample = dict(
          image=make_image(),
          bounding_boxes=make_bounding_boxes(
              format=BoundingBoxFormat.XYXY
          ),
          video=make_video(),
          str="str",
          int=0,
      )
  
      transform = transforms.TransposeDimensions(dims)
      transformed_sample = transform(sample)
  
      for key, value in sample.items():
          value_type = type(value)
          transformed_value = transformed_sample[key]
  
          transposed_dims = transform.dims.get(value_type)
          if check_type(value, (Image, is_pure_tensor, Video)):
              if transposed_dims is not None:
                  assert transformed_value.transpose(
                      *transposed_dims
                  ).equal(value)
              assert type(transformed_value) == torch.Tensor
          else:
              assert transformed_value is value
  
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 the TestTransposeDimensions obj's underlying member method code with example usage: obj.test_call(dims) without return types