code: def normalize(
      tensor: Tensor,
      mean: List[float],
      std: List[float],
      inplace: bool = False,
  ) -> Tensor:
      """Normalize a float tensor image with mean and standard deviation.
      This transform does not support PIL Image.
  
      .. note::
          This transform acts out of place by default, i.e., it does not mutates the input tensor.
  
      See :class:`~torchvision.transforms.Normalize` for more details.
  
      Args:
          tensor (Tensor): Float tensor image of size (C, H, W) or (B, C, H, W) to be normalized.
          mean (sequence): Sequence of means for each channel.
          std (sequence): Sequence of standard deviations for each channel.
          inplace(bool,optional): Bool to make this operation inplace.
  
      Returns:
          Tensor: Normalized Tensor image.
      """
      if not torch.jit.is_scripting() and not torch.jit.is_tracing():
          _log_api_usage_once(normalize)
      if not isinstance(tensor, torch.Tensor):
          raise TypeError(
              f"img should be Tensor Image. Got {type(tensor)}"
          )
  
      return F_t.normalize(tensor, mean=mean, std=std, inplace=inplace)
  
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 11 for calling a global function with example usage: normalize(tensor, mean, std, inplace) and returns: F_t