class_docstr: Model for Generative Adversarial Networks.
  
  Arguments:
      layers: Generative Adversarial layer container
      noise_dim (Tuple): Dimensionality of the noise feeding the generator
      noise_type (Str): Noise distribution, 'normal' (default) or 'uniform'
      weights_only (bool): set to True if you do not want to recreate layers
                           and states during deserialization from a serialized model
                           description.  Defaults to False.
      name (str): Model name.  Defaults to "model"
      optimizer (Optimizer): Optimizer object which defines the learning rule for updating
                             model parameters (i.e., GradientDescentMomentum, Adadelta)
      k (int): Number of data batches per noise batch
      wgan_param_clamp (float or None): In case of WGAN weight clamp value, None for others
      wgan_train_sched (bool): Whether to use the FAIR WGAN training schedule of critics
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docstr: WGAN training schedule for generator following Arjovsky et al. 2017
  
  Arguments:
      giter (int): Counter for generator iterations
docstr_tok: ["WGAN","training","schedule","for","generator","following","Arjovsky","et","al",".","2017","Arguments",":","giter","(","int",")",":","Counter","for","generator","iterations"]
code: def get_k(self, giter):
      """
      WGAN training schedule for generator following Arjovsky et al. 2017
  
      Arguments:
          giter (int): Counter for generator iterations
      """
      if self.wgan_train_sched and (giter < 25 or giter % 500 == 0):
          return 100
      else:
          return self.k
  
imports: from __future__ import division, print_function
  from builtins import str, zip
  from collections import OrderedDict
  import logging
  from neon import __version__
  from neon import NervanaObject, logger
  from neon.backends.backend import Block
  from neon.transforms import CrossEntropyBinary, Logistic
  from neon.util.persist import load_obj, save_obj, load_class
  from neon.util.modeldesc import ModelDescription
  from neon.layers import Sequential, Activation
  from neon.layers.container import DeltasTree, SkipThought
  from neon.util.beamsearch import BeamSearch
  from neon.optimizers.optimizer import get_param_list
  import numpy
label_desc: Use image node_id 5 for calling the GAN obj's underlying member method code with example usage: obj.get_k(giter) and returns: int, self