class_docstr: Image Augmentation.
  
  Base class for applying real-time augmentation related to images.
  
  This class is meant to be used as an argument of `input_data`. When training
  a model, the defined augmentation methods will be applied at training
  time only. Note that ImagePreprocessing is similar to ImageAugmentation,
  but applies at both training time and testing time.
  
  Arguments:
      None.
  
  Parameters:
      methods: `list of function`. The augmentation methods to apply.
      args: A `list` of arguments list to use for these methods.
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docstr: add_random_flip_leftright.
  
  Randomly flip an image (left to right).
  
  Returns:
      Nothing.
docstr_tok: ["add_random_flip_leftright",".","Randomly","flip","an","image","(","left","to","right",")",".","Returns",":","Nothing","."]
code: def add_random_flip_leftright(self):
      """add_random_flip_leftright.
  
      Randomly flip an image (left to right).
  
      Returns:
          Nothing.
  
      """
      self.methods.append(self._random_flip_leftright)
      self.args.append(None)
  
imports: from __future__ import division, print_function, absolute_import
  import random
  import numpy
label_desc: Use image node_id 3 for calling the ImageAugmentation obj's underlying member method code with example usage: obj.add_random_flip_leftright() without return types