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_blur.
  
  Randomly blur an image by applying a gaussian filter with a random
  sigma (0., sigma_max).
  
  Arguments:
      sigma: `float` or list of `float`. Standard deviation for Gaussian
          kernel. The standard deviations of the Gaussian filter are
          given for each axis as a sequence, or as a single number,
          in which case it is equal for all axes.
  
  Returns:
      Nothing.
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code: def add_random_blur(self, sigma_max=5.0):
      """add_random_blur.
  
      Randomly blur an image by applying a gaussian filter with a random
      sigma (0., sigma_max).
  
      Arguments:
          sigma: `float` or list of `float`. Standard deviation for Gaussian
              kernel. The standard deviations of the Gaussian filter are
              given for each axis as a sequence, or as a single number,
              in which case it is equal for all axes.
  
      Returns:
          Nothing.
  
      """
      self.methods.append(self._random_blur)
      self.args.append([sigma_max])
  
imports: from __future__ import division, print_function, absolute_import
  import random
  import numpy
label_desc: Use image node_id 7 for calling the ImageAugmentation obj's underlying member method code with example usage: obj.add_random_blur(sigma_max) without return types