code: def get_iterator(self, dataset, training=False):
          
          if training:
              kwargs = self._get_params_for('iterator_train')
              iterator = self.iterator_train
          else:
              kwargs = self._get_params_for('iterator_valid')
              iterator = self.iterator_valid
  
          if 'batch_size' not in kwargs:
              kwargs['batch_size'] = self.batch_size
  
          if kwargs['batch_size'] == -1:
              kwargs['batch_size'] = len(dataset)
  
          return iterator(dataset, **kwargs)
docs: Get an iterator that allows to loop over the batches of the
          given data.
  
          If ``self.iterator_train__batch_size`` and/or
          ``self.iterator_test__batch_size`` are not set, use
          ``self.batch_size`` instead.
  
          Parameters
          ----------
          dataset : torch Dataset (default=skorch.dataset.Dataset)
            Usually, ``self.dataset``, initialized with the corresponding
            data, is passed to ``get_iterator``.
  
          training : bool (default=False)
            Whether to use ``iterator_train`` or ``iterator_test``.
  
          Returns
          -------
          iterator
            An instantiated iterator that allows to loop over the
            mini-batches.