code: def tensor_normalize(tensor, mean, std, func=None):
      """
      Normalize a given tensor by subtracting the mean and dividing the std.
      Args:
          tensor (tensor): tensor to normalize.
          mean (tensor or list): mean value to subtract.
          std (tensor or list): std to divide.
      """
      if tensor.dtype == torch.uint8:
          tensor = tensor.float()
          tensor = tensor / 255.0
      if type(mean) == list:
          mean = torch.tensor(mean)
      if type(std) == list:
          std = torch.tensor(std)
      if func is not None:
          tensor = func(tensor)
      tensor = tensor - mean
      tensor = tensor / std
      return tensor
  
imports: import logging
  import numpy
  import os
  import random
  import time
  from collections import defaultdict
  import cv2
  import torch
  from torch.utils.data.distributed import DistributedSampler
  from torchvision import transforms
  from slowfast.utils.env import pathmgr
  from .None import transform
  from .random_erasing import RandomErasing
  from .transform import create_random_augment
label_desc: Use image node_id 9 for calling a global function with example usage: tensor_normalize(tensor, mean, std, func) and returns: tensor