class_docstr: Produces [image, label] in Cifar10/100 dataset,
  image is 32x32x3 in the range [0,255].
  label is an int.
docstr: Args:
      names (tuple[str]): the names ('train' or 'test') of the datasets
  
  Returns:
      An array of three values as mean of each channel, for all images in the given datasets.
docstr_tok: ["Args",":","names","(","tuple","[","str","]",")",":","the","names","(","'train","'","or","'test","'",")","of","the","datasets","Returns",":","An","array","of","three","values","as","mean","of","each","channel",",","for","all","images","in","the","given","datasets","."]
code: def get_per_channel_mean(self, names=("train", "test")):
      """
      Args:
          names (tuple[str]): the names ('train' or 'test') of the datasets
  
      Returns:
          An array of three values as mean of each channel, for all images in the given datasets.
      """
      mean = self.get_per_pixel_mean(names)
      return np.mean(mean, axis=(0, 1))
  
imports: import numpy
  import os
  import pickle
  import tarfile
  from ...utils import logger
  from ...utils.fs import download, get_dataset_path
  from ..base import RNGDataFlow
label_desc: Use image node_id 6 for calling the CifarBase obj's underlying member method code with example usage: obj.get_per_channel_mean(names) and returns: np