code: def _check_inputs(self, flat_inputs: List[Any]) -> None:
      if not has_any(
          flat_inputs,
          PIL.Image.Image,
          tv_tensors.Image,
          is_pure_tensor,
          tv_tensors.Video,
      ):
          raise TypeError(
              f"{type(self).__name__}() requires input sample to contain an tensor or PIL image or a Video."
          )
  
      if has_any(flat_inputs, tv_tensors.BoundingBoxes) and not has_any(
          flat_inputs, Label, OneHotLabel
      ):
          raise TypeError(
              f"If a BoundingBoxes is contained in the input sample, "
              f"{type(self).__name__}() also requires it to contain a Label or OneHotLabel."
          )
  
file_path: vision/torchvision/prototype/transforms/_geometry.py
imports: from typing import Any, Dict, List, Optional, Sequence, Type, Union
  import PIL.Image
  import torch
  from torchvision import tv_tensors
  from torchvision.prototype.tv_tensors import Label, OneHotLabel
  from torchvision.transforms.v2 import functional, Transform
  from torchvision.transforms.v2._utils import _FillType, _get_fill, _setup_fill_arg, _setup_size, get_bounding_boxes, has_any, is_pure_tensor, query_size
label_desc: Use image node_id 2 for calling the FixedSizeCrop obj's underlying member method code with example usage: obj._check_inputs(flat_inputs) without return types