class_docstr: Normalize a tensor image or video with mean and standard deviation.
  
  This transform does not support PIL Image.
  Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
  channels, this transform will normalize each channel of the input
  ``torch.*Tensor`` i.e.,
  ``output[channel] = (input[channel] - mean[channel]) / std[channel]``
  
  .. note::
      This transform acts out of place, i.e., it does not mutate the input tensor.
  
  Args:
      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 in-place.
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code: def __init__(
      self,
      mean: Sequence[float],
      std: Sequence[float],
      inplace: bool = False,
  ):
      super().__init__()
      self.mean = list(mean)
      self.std = list(std)
      self.inplace = inplace
  
imports: import warnings
  from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Type, Union
  import PIL.Image
  import torch
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from torchvision import transforms, tv_tensors
  from torchvision.transforms.v2 import functional, Transform
  from ._utils import _parse_labels_getter, _setup_number_or_seq, _setup_size, get_bounding_boxes, has_any, is_pure_tensor
label_desc: Use image node_id 1 to create a new Normalize object from inherited base classes: Transform with example: obj = Normalize(mean, std, inplace)