code: def forward(self, vid: Tensor) -> Tensor:
      need_squeeze = False
      if vid.ndim < 5:
          vid = vid.unsqueeze(dim=0)
          need_squeeze = True
  
      N, T, C, H, W = vid.shape
      vid = vid.view(-1, C, H, W)
      # We hard-code antialias=False to preserve results after we changed
      # its default from None to True (see
      # https://github.com/pytorch/vision/pull/7160)
      # TODO: we could re-train the video models with antialias=True?
      vid = F.resize(
          vid,
          self.resize_size,
          interpolation=self.interpolation,
          antialias=False,
      )
      vid = F.center_crop(vid, self.crop_size)
      vid = F.convert_image_dtype(vid, torch.float)
      vid = F.normalize(vid, mean=self.mean, std=self.std)
      H, W = self.crop_size
      vid = vid.view(N, T, C, H, W)
      vid = vid.permute(
          0, 2, 1, 3, 4
      )  # (N, T, C, H, W) => (N, C, T, H, W)
  
      if need_squeeze:
          vid = vid.squeeze(dim=0)
      return vid
  
imports: from typing import Optional, Tuple
  import torch
  from torch import nn, Tensor
  from .None import functional, InterpolationMode
label_desc: Use image node_id 2 for calling the VideoClassification obj's underlying member method code with example usage: obj.forward(vid) and returns: vid