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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docstr: add_samplewise_zero_center.
  
  Zero center every sample with specified mean. If not specified,
  the mean is evaluated over all samples.
  
  Arguments:
      mean: `float` (optional). Provides a custom mean. If none
          provided, it will be automatically caluclated based on
          the training dataset. Default: None.
  
  Returns:
      Nothing.
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code: def add_featurewise_zero_center(self, mean=None):
      """add_samplewise_zero_center.
  
      Zero center every sample with specified mean. If not specified,
      the mean is evaluated over all samples.
  
      Arguments:
          mean: `float` (optional). Provides a custom mean. If none
              provided, it will be automatically caluclated based on
              the training dataset. Default: None.
  
      Returns:
          Nothing.
  
      """
      self.global_mean.is_required = True
      self.global_mean.value = mean
      self.methods.append(self._featurewise_zero_center)
      self.args.append(None)
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import pickle
  import tensorflow.compat.v1
label_desc: Use image node_id 8 for calling the DataPreprocessing obj's underlying member method code with example usage: obj.add_featurewise_zero_center(mean) without return types