class_docstr: Original code from torchvision: https://github.com/pytorch/vision/tree/main/torchvision/transforms
  
  Args:
      clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
  Returns:
      torch.tensor: central cropping of video clip. Size is
      (C, T, crop_size, crop_size)
class_docstr_tok: ["Original","code","from","torchvision",":","https",":","\/\/github.com\/pytorch\/vision\/tree\/main\/torchvision\/transforms","Args",":","clip","(","torch.tensor",")",":","Video","clip","to","be","cropped",".","Size","is","(","C",",","T",",","H",",","W",")","Returns",":","torch.tensor",":","central","cropping","of","video","clip",".","Size","is","(","C",",","T",",","crop_size",",","crop_size",")"]
code: def __call__(self, clip):
      assert clip.ndimension() == 4
      h, w = clip.size(-2), clip.size(-1)
      th, tw = self.crop_size
      if h < th or w < tw:
          raise ValueError(
              "height and width must be no smaller than crop_size"
          )
  
      i = int(round((h - th) / 2.0))
      j = int(round((w - tw) / 2.0))
      return clip[..., i : i + th, j : j + tw]
  
imports: import os
  import logging
  import numpy
  import numbers
  import torch
  from torch import nn
  from torchvision.transforms import Compose
label_desc: Use image node_id 2 for calling the CenterCropVideo obj's underlying member method code with example usage: obj.__call__(clip) and returns: clip