old_contents: """Base class for the components in other modules."""
  import numpy
  import theano
  from theano import tensor
  
  #from pylearn.gd.sgd import sgd_updates
  #from pylearn.algorithms.mcRBM import contrastive_cost, contrastive_grad
  theano.config.warn.sum_div_dimshuffle_bug = False
  floatX = theano.config.floatX
  
  if 0:
      print 'WARNING: using SLOW rng'
      RandomStreams = tensor.shared_randomstreams.RandomStreams
  else:
      import theano.sandbox.rng_mrg
      RandomStreams = theano.sandbox.rng_mrg.MRG_RandomStreams
  
  class Block(object):
      """
      Basic building block for deep architectures.
      """
      def params(self):
          """
          Returns a list of *shared* learnable parameters that
          are, in your judgment, typically learned in this
          model.
          """
          # NOTE: We return list(self._params) rather than self._params
          # in order to explicitly make a copy, so that the list isn't
          # absentmindedly modified. If a user really knows what they're
          # doing they can modify self._params.
          return list(self._params)
  
      def __call__(self, inputs):
          raise NotImplementedError('__call__')
  
  class Trainer(object):
      """
      Basic abstract class for training
      """
      def updates(self):
          """Do one step of training."""
          raise NotImplementedError()
      
      def function(self, input):
          """Return a compiled Theano function for training"""
          raise NotImplementedError()
  
      def save(self, save_dir, save_filename):
          raise NotImplementedError('save')
  
new_contents: """Base class for the components in other modules."""
  import theano
  from theano import tensor
  
  #from pylearn.gd.sgd import sgd_updates
  #from pylearn.algorithms.mcRBM import contrastive_cost, contrastive_grad
  theano.config.warn.sum_div_dimshuffle_bug = False
  floatX = theano.config.floatX
  
  if 0:
      print 'WARNING: using SLOW rng'
      RandomStreams = tensor.shared_randomstreams.RandomStreams
  else:
      import theano.sandbox.rng_mrg
      RandomStreams = theano.sandbox.rng_mrg.MRG_RandomStreams
  
  class Block(object):
      """
      Basic building block for deep architectures.
      """
      def params(self):
          """
          Returns a list of *shared* learnable parameters that
          are, in your judgment, typically learned in this
          model.
          """
          # NOTE: We return list(self._params) rather than self._params
          # in order to explicitly make a copy, so that the list isn't
          # absentmindedly modified. If a user really knows what they're
          # doing they can modify self._params.
          return list(self._params)
  
      def __call__(self, inputs):
          raise NotImplementedError('__call__')
  
  class Trainer(object):
      """
      Basic abstract class for training
      """
      def updates(self):
          """Do one step of training."""
          raise NotImplementedError()
      
      def function(self, input):
          """Return a compiled Theano function for training"""
          raise NotImplementedError()
  
      def save(self, save_dir, save_filename):
          raise NotImplementedError('save')
  
subject: Remove now useless import.