code: def __init__(
      self,
      size: Union[int, Sequence[int]],
      fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0,
      padding_mode: str = "constant",
  ) -> None:
      super().__init__()
      size = tuple(
          _setup_size(
              size,
              error_msg="Please provide only two dimensions (h, w) for size.",
          )
      )
      self.crop_height = size[0]
      self.crop_width = size[1]
  
      self.fill = fill
      self._fill = _setup_fill_arg(fill)
  
      self.padding_mode = padding_mode
  
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 1 to create a new FixedSizeCrop object from inherited base classes: Transform with example: obj = FixedSizeCrop(size, fill, padding_mode)