class_docstr: Image Preprocessing.
  
  Base class for applying real-time image related pre-processing.
  
  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 ImageAugmentation is similar to
  ImagePreprocessing, but only applies at training time.
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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.
      per_channel: `bool`. If True, compute mean per color channel.
  
  Returns:
      Nothing.
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code: def add_featurewise_zero_center(self, mean=None, per_channel=False):
      """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.
          per_channel: `bool`. If True, compute mean per color channel.
  
      Returns:
          Nothing.
  
      """
      self.global_mean.is_required = True
      self.global_mean.value = mean
      if per_channel:
          self.global_mean_pc = True
      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 10 for calling the ImagePreprocessing obj's underlying member method code with example usage: obj.add_featurewise_zero_center(mean, per_channel) without return types