code: def iterate(self, params, repetition, iteration):
      
      objname, sensations = self.objects.items()[iteration]
  
      # Select sensations to infer
      np.random.shuffle(sensations[0])
      sensations = [sensations[0][:self.numOfSensations]]
  
      self.network.sendReset()
  
      # Collect all statistics for every inference.
      # See L246aNetwork._updateInferenceStats
      stats = defaultdict(list)
      self.network.infer(sensations=sensations, stats=stats, objname=objname)
      stats.update({"name": objname})
      return stats
docs: For each iteration try to infer the object represented by the 'iteration'
      parameter returning Whether or not the object was unambiguously classified.
      :param params: Specific parameters for this iteration. See 'experiments.cfg'
                     for list of parameters
      :param repetition: Current repetition
      :param iteration: Use the iteration to select the object to infer
      :return: Whether or not the object was classified