How to use the skorch.dataset.get_len function in skorch

def _get_batches_per_epoch_phase(self, net, X, training):
        if X is None:
            return 0
        batch_size = self._get_batch_size(net, training)
        return int(np.ceil(get_len(X) / batch_size))

def _get_batches_per_epoch_phase(self, net, dataset, training):
        if dataset is None:
            return 0
        batch_size = self._get_batch_size(net, training)
        return int(np.ceil(get_len(dataset) / batch_size))

self.notify('on_batch_begin', X=Xi, y=yi_res, training=True)
                step = self.train_step(Xi, yi, **fit_params)
                self.history.record_batch('train_loss', step['loss'].item())
                self.history.record_batch('train_batch_size', get_len(Xi))
                self.notify('on_batch_end', X=Xi, y=yi_res, training=True, **step)

            if dataset_valid is None:
                self.notify('on_epoch_end', **on_epoch_kwargs)
                continue

            for Xi, yi in self.get_iterator(dataset_valid, training=False):
                yi_res = yi if not y_valid_is_ph else None
                self.notify('on_batch_begin', X=Xi, y=yi_res, training=False)
                step = self.validation_step(Xi, yi, **fit_params)
                self.history.record_batch('valid_loss', step['loss'].item())
                self.history.record_batch('valid_batch_size', get_len(Xi))
                self.notify('on_batch_end', X=Xi, y=yi_res, training=False, **step)

            self.notify('on_epoch_end', **on_epoch_kwargs)
        return self

self.notify('on_batch_end', X=Xi, y=yi_res, training=True, **step)
            self.history.record("train_batch_count", train_batch_count)

            if dataset_valid is None:
                self.notify('on_epoch_end', **on_epoch_kwargs)
                continue

            valid_batch_count = 0
            for data in self.get_iterator(dataset_valid, training=False):
                Xi, yi = unpack_data(data)
                yi_res = yi if not y_valid_is_ph else None
                self.notify('on_batch_begin', X=Xi, y=yi_res, training=False)
                step = self.validation_step(Xi, yi, **fit_params)
                valid_batch_count += 1
                self.history.record_batch('valid_loss', step['loss'].item())
                self.history.record_batch('valid_batch_size', get_len(Xi))
                self.notify('on_batch_end', X=Xi, y=yi_res, training=False, **step)
            self.history.record("valid_batch_count", valid_batch_count)

            self.notify('on_epoch_end', **on_epoch_kwargs)
        return self