code: def normalize(
      inpt: torch.Tensor,
      mean: List[float],
      std: List[float],
      inplace: bool = False,
  ) -> torch.Tensor:
      """See :class:`~torchvision.transforms.v2.Normalize` for details."""
      if torch.jit.is_scripting():
          return normalize_image(
              inpt, mean=mean, std=std, inplace=inplace
          )
  
      _log_api_usage_once(normalize)
  
      kernel = _get_kernel(normalize, type(inpt))
      return kernel(inpt, mean=mean, std=std, inplace=inplace)
  
imports: import math
  from typing import List, Optional
  import PIL.Image
  import torch
  from torch.nn.functional import conv2d, pad
  from torchvision import tv_tensors
  from torchvision.transforms._functional_tensor import _max_value
  from torchvision.transforms.functional import pil_to_tensor, to_pil_image
  from torchvision.utils import _log_api_usage_once
  from ._utils import _get_kernel, _register_kernel_internal
label_desc: Use image node_id 1 for calling a global function with example usage: normalize(inpt, mean, std, inplace) and returns: kernel, normalize_image