code: def resized_crop(
      img: Tensor,
      top: int,
      left: int,
      height: int,
      width: int,
      size: List[int],
      interpolation: InterpolationMode = InterpolationMode.BILINEAR,
      antialias: Optional[bool] = True,
  ) -> Tensor:
      """Crop the given image and resize it to desired size.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
  
      Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.
  
      Args:
          img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
          top (int): Vertical component of the top left corner of the crop box.
          left (int): Horizontal component of the top left corner of the crop box.
          height (int): Height of the crop box.
          width (int): Width of the crop box.
          size (sequence or int): Desired output size. Same semantics as ``resize``.
          interpolation (InterpolationMode): Desired interpolation enum defined by
              :class:`torchvision.transforms.InterpolationMode`.
              Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
              ``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are
              supported.
              The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
          antialias (bool, optional): Whether to apply antialiasing.
              It only affects **tensors** with bilinear or bicubic modes and it is
              ignored otherwise: on PIL images, antialiasing is always applied on
              bilinear or bicubic modes; on other modes (for PIL images and
              tensors), antialiasing makes no sense and this parameter is ignored.
              Possible values are:
  
              - ``True`` (default): will apply antialiasing for bilinear or bicubic modes.
                Other mode aren't affected. This is probably what you want to use.
              - ``False``: will not apply antialiasing for tensors on any mode. PIL
                images are still antialiased on bilinear or bicubic modes, because
                PIL doesn't support no antialias.
              - ``None``: equivalent to ``False`` for tensors and ``True`` for
                PIL images. This value exists for legacy reasons and you probably
                don't want to use it unless you really know what you are doing.
  
              The default value changed from ``None`` to ``True`` in
              v0.17, for the PIL and Tensor backends to be consistent.
      Returns:
          PIL Image or Tensor: Cropped image.
      """
      if not torch.jit.is_scripting() and not torch.jit.is_tracing():
          _log_api_usage_once(resized_crop)
      img = crop(img, top, left, height, width)
      img = resize(img, size, interpolation, antialias=antialias)
      return img
  
imports: import math
  import numbers
  import sys
  import warnings
  from enum import Enum
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  import torch
  from PIL import Image
  from torch import Tensor
  from ..utils import _log_api_usage_once
  from .None import _functional_pil, _functional_tensor
label_desc: Use image node_id 17 for calling a global function with example usage: resized_crop(img, top, left, height, width, size, interpolation, antialias) and returns: img