class_docstr: Data Preprocessing.
  
  Base class for applying common real-time data preprocessing.
  
  This class is meant to be used as an argument of `input_data`. When training
  a model, the defined pre-processing methods will be applied at both
  training and testing time. Note that DataAugmentation is similar to
  DataPreprocessing, but only applies at training time.
  
  Arguments:
      None.
  
  Parameters:
      methods: `list of function`. Augmentation methods to apply.
      args: A `list` of arguments to use for these methods.
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code: def _zca_whitening(self, batch):
      for i in range(len(batch)):
          flat = np.reshape(batch[i], batch[i].size)
          white = np.dot(flat, self.global_pc.value)
          s1, s2, s3 = (
              batch[i].shape[0],
              batch[i].shape[1],
              batch[i].shape[2],
          )
          batch[i] = np.reshape(white, (s1, s2, s3))
      return batch
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import pickle
  import tensorflow.compat.v1
label_desc: Use image node_id 15 for calling the DataPreprocessing obj's underlying member method code with example usage: obj._zca_whitening(batch) and returns: batch