class_docstr: Class which stores a list of layers describing the model. Can train the layer
  weights on a dataset, evaluate on a test set and serialize the model.
  Additional functionality can be added to the fit method through callback functions.
  
  Arguments:
      layers: layer container, a list of layers (that will be containerized),
              or a serialized model description
      dataset (NervanaDataIterator): Data set (ignored, will be removed)
      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)
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docstr: Set the actual minibatch size, so even though the buffers are allocated considering
  excessive padding, the processing for some layers may be shortened.
  Currently most of the neon layers don't use that to control the processing. The
  interface is here only for when someone wants to set that information and experiment.
  
  Arguments:
      N:
  
  Returns:
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code: def set_batch_size(self, N):
      """
      Set the actual minibatch size, so even though the buffers are allocated considering
      excessive padding, the processing for some layers may be shortened.
      Currently most of the neon layers don't use that to control the processing. The
      interface is here only for when someone wants to set that information and experiment.
  
      Arguments:
          N:
  
      Returns:
  
      """
      return self.layers.set_batch_size(N)
  
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 20 for calling the Model obj's underlying member method code with example usage: obj.set_batch_size(N) and returns: self