class_docstr: Normalize a ``numpy.array`` image 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
  ``numpy.array`` 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 array.
  
  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.
code: def __init__(self, mean, std, inplace=False):
      super().__init__()
      self.mean = mean
      self.std = std
      self.inplace = inplace
  
file_path: singa/examples/demos/Classification/BloodMnist/transforms.py
imports: import numpy
  from PIL import Image
label_desc: Use image node_id 1 to create a new Normalize object from inherited base classes: object with example: obj = Normalize(mean, std, inplace)