code: def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
      if params["needs_crop"]:
          inpt = self._call_kernel(
              F.crop,
              inpt,
              top=params["top"],
              left=params["left"],
              height=params["height"],
              width=params["width"],
          )
  
      if params["is_valid"] is not None:
          if isinstance(inpt, (Label, OneHotLabel, tv_tensors.Mask)):
              inpt = tv_tensors.wrap(
                  inpt[params["is_valid"]], like=inpt
              )
          elif isinstance(inpt, tv_tensors.BoundingBoxes):
              inpt = tv_tensors.wrap(
                  F.clamp_bounding_boxes(
                      inpt[params["is_valid"]],
                      format=inpt.format,
                      canvas_size=inpt.canvas_size,
                  ),
                  like=inpt,
              )
  
      if params["needs_pad"]:
          fill = _get_fill(self._fill, type(inpt))
          inpt = self._call_kernel(
              F.pad,
              inpt,
              params["padding"],
              fill=fill,
              padding_mode=self.padding_mode,
          )
  
      return inpt
  
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 4 for calling the FixedSizeCrop obj's underlying member method code with example usage: obj._transform(inpt, params) and returns: inpt