code: def _check_image_or_video(
      self, inpt: torch.Tensor, *, batch_size: int
  ):
      expected_num_dims = 5 if isinstance(inpt, tv_tensors.Video) else 4
      if inpt.ndim != expected_num_dims:
          raise ValueError(
              f"Expected a batched input with {expected_num_dims} dims, but got {inpt.ndim} dimensions instead."
          )
      if inpt.shape[0] != batch_size:
          raise ValueError(
              f"The batch size of the image or video does not match the batch size of the labels: "
              f"{inpt.shape[0]} != {batch_size}."
          )
  
imports: import math
  import numbers
  import warnings
  from typing import Any, Callable, Dict, List, Tuple
  import PIL.Image
  import torch
  from torch.nn.functional import one_hot
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from torchvision import transforms, tv_tensors
  from torchvision.transforms.v2 import functional
  from ._transform import _RandomApplyTransform, Transform
  from ._utils import _parse_labels_getter, has_any, is_pure_tensor, query_chw, query_size
label_desc: Use image node_id 3 for calling the _BaseMixUpCutMix obj's underlying member method code with example usage: obj._check_image_or_video(inpt) without return types