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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code: def _random_90degrees_rotation(self, batch, rotations=[0, 1, 2, 3]):
      for i in range(len(batch)):
          num_rotations = random.choice(rotations)
          batch[i] = np.rot90(batch[i], num_rotations)
  
      return batch
  
imports: from __future__ import division, print_function, absolute_import
  import random
  import numpy
label_desc: Use image node_id 11 for calling the ImageAugmentation obj's underlying member method code with example usage: obj._random_90degrees_rotation(batch, rotations) and returns: batch