How to use the skorch.callbacks.EpochScoring function in skorch

return ds, skorch.dataset.Dataset(corpus.valid[:200], y=None)

### Perform training
net = skorch.NeuralNetClassifier(
    module=wavetorch.WaveCell,

    # Training configuration
    max_epochs=cfg['training']['N_epochs'],
    batch_size=cfg['training']['batch_size'],
    lr=cfg['training']['lr'],
    # train_split=skorch.dataset.CVSplit(cfg['training']['N_folds'], stratified=True, random_state=cfg['seed']),
    optimizer=torch.optim.Adam,
    criterion=torch.nn.CrossEntropyLoss,
    callbacks=[
        ClipDesignRegion,
        skorch.callbacks.EpochScoring('accuracy', lower_is_better=False, on_train=True, name='train_acc'),
        skorch.callbacks.Checkpoint(monitor=None, fn_prefix='1234_', dirname='test', f_params="params_{last_epoch[epoch]}.pt", f_optimizer='optimizer.pt', f_history='history.json')
        ],
    callbacks__print_log__keys_ignored=None,
    train_split=None,

    # These al get passed as options to WaveCell
    module__Nx=cfg['geom']['Nx'],
    module__Ny=cfg['geom']['Ny'],
    module__h=cfg['geom']['h'],
    module__dt=cfg['geom']['dt'],
    module__init=cfg['geom']['init'], 
    module__c0=cfg['geom']['c0'], 
    module__c1=cfg['geom']['c1'], 
    module__sigma=cfg['geom']['pml']['max'], 
    module__N=cfg['geom']['pml']['N'], 
    module__p=cfg['geom']['pml']['p'],

y_test,
        batch_size,
        device,
        lr,
        max_epochs,
):
    torch.manual_seed(0)
    net = NeuralNetClassifier(
        ClassifierModule,
        batch_size=batch_size,
        optimizer=torch.optim.Adadelta,
        lr=lr,
        device=device,
        max_epochs=max_epochs,
        callbacks=[
            ('tr_acc', EpochScoring(
                'accuracy',
                lower_is_better=False,
                on_train=True,
                name='train_acc',
            )),
        ],
    )
    net.fit(X_train, y_train)
    y_pred = net.predict(X_test)
    score = accuracy_score(y_test, y_pred)
    return score

def _default_callbacks(self):
        return [
            ('epoch_timer', EpochTimer()),
            ('train_loss', BatchScoring(
                train_loss_score,
                name='train_loss',
                on_train=True,
                target_extractor=noop,
            )),
            ('valid_loss', BatchScoring(
                valid_loss_score,
                name='valid_loss',
                target_extractor=noop,
            )),
            ('valid_acc', EpochScoring(
                'accuracy',
                name='valid_acc',
                lower_is_better=False,
            )),
            ('print_log', PrintLog()),
        ]