class_docstr: Normalize the (CTHW) video clip by mean subtraction and division by standard deviation
  
  Args:
      mean (3-tuple): pixel RGB mean
      std (3-tuple): pixel RGB standard deviation
      inplace (boolean): whether do in-place normalization
class_docstr_tok: ["Normalize","the","(","CTHW",")","video","clip","by","mean","subtraction","and","division","by","standard","deviation","Args",":","mean","(","3-tuple",")",":","pixel","RGB","mean","std","(","3-tuple",")",":","pixel","RGB","standard","deviation","inplace","(","boolean",")",":","whether","do","in-place","normalization"]
docstr: Args:
      x (torch.Tensor): video tensor with shape (C, T, H, W).
docstr_tok: ["Args",":","x","(","torch.Tensor",")",":","video","tensor","with","shape","(","C",",","T",",","H",",","W",")","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Args:
          x (torch.Tensor): video tensor with shape (C, T, H, W).
      """
      vid = x.permute(1, 0, 2, 3)  # C T H W to T C H W
      vid = super().forward(vid)
      vid = vid.permute(1, 0, 2, 3)  # T C H W to C T H W
      return vid
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import pytorchvideo.transforms.functional
  import torch
  import torchvision.transforms
label_desc: Use image node_id 1 for calling the Normalize obj's underlying member method code with example usage: obj.forward(x) and returns: vid