Hi, I implemented a train_and_evaluate_model ias follows:                                                                                                                                                                                                              
from sklearn.model_selection import KFold
from torch.optim.lr_scheduler import ReduceLROnPlateau


def train_and_evaluate_model(model, learning_rate = 0.01, epochs = 1000, optimization_technique=None, k_splits=None, batch_size=64, patience=None, scheduler_patience=None, **kwargs):
    epoch_train_losses = []
    epoch_train_accuracies = []
    epoch_test_losses = []
    epoch_test_accuracies = []

    if optimization_technique == 'k_fold' and k_splits:
        kfold = KFold(n_splits=k_splits, shuffle=True)
    else:
        kfold = KFold(n_splits=1, shuffle=False)

    for train_index, test_index in kfold.split(X_scaled):
        X_train, X_test = X_scaled[train_index], X_scaled[test_index]
        y_train, y_test = y[train_index], y[test_index]

        X_train, X_test = torch.tensor(X_train, dtype=torch.float32), torch.tensor(X_test, dtype=torch.float32)
        y_train, y_test = torch.tensor(y_train, dtype=torch.float32).view(-1, 1), torch.tensor(y_test, dtype=torch.float32).view(-1, 1)

        optimizer = optim.SGD(model.parameters(), lr=learning_rate)
        criterion = nn.BCELoss()
        scheduler = ReduceLROnPlateau(optimizer, 'min', patience=scheduler_patience, verbose=True) if optimization_technique == 'learning_rate_scheduler' else None
        
        best_loss = float('inf')
        stopping_counter = 0
        
        for epoch in range(epochs):
            optimizer.zero_grad()
            y_pred_train = model(X_train)
            loss = criterion(y_pred_train, y_train)
            loss.backward()
            optimizer.step()

            with torch.no_grad():
                y_pred_test = model(X_test)
                test_loss = criterion(y_pred_test, y_test)

            epoch_train_losses.append(loss.item())
            epoch_train_accuracies.extend((y_pred_train > 0.5).float().numpy().flatten().tolist())
            epoch_train_accuracies.append(accuracy_score(y_train, (y_pred_train > 0.5).float()))

            epoch_test_losses.append(test_loss.item())
            epoch_test_accuracies.extend((y_pred_test > 0.5).float().numpy().flatten().tolist())
            epoch_test_accuracies.append(accuracy_score(y_test, (y_pred_test > 0.5).float()))

            if optimization_technique == 'early_stopping' and patience:
                if test_loss < best_loss:
                    best_loss = test_loss
                    stopping_counter = 0
                else:
                    stopping_counter += 1
                if stopping_counter > patience:
                    break

            if optimization_technique == 'learning_rate_scheduler' and scheduler_patience and scheduler:
                scheduler.step(test_loss)

            if optimization_technique == 'k_fold' and k_splits:
                if epoch == 999:
                    break

        if optimization_technique != 'k_fold':
            break

    return epoch_train_losses, epoch_train_accuracies, epoch_test_losses, epoch_test_accuracies
This line here  train_losses, train_accuracies, test_losses, test_accuracies = train_and_evaluate_model(model, “dropout”) throws a kfold error at for loop  since I'm not passing any value for kfold parameter. So, Modify the above code to also work for finding the best_model which is defined as # Step 2: Train the classifier with three different dropout values
dropout_values = [0.3, 0.5, 0.7]
models = []
losses = []
accuracies = []

for dropout in dropout_values:
    model = BetterNNClassifier(input_size, hidden_size, output_size, dropout)
    # Train the model and get training and testing losses and accuracies
    train_losses, train_accuracies, test_losses, test_accuracies = train_and_evaluate_model(model)
    models.append(model)
    losses.append((train_losses, test_losses))
    accuracies.append((train_accuracies, test_accuracies))

# Step 3: Choose the best dropout value (based on the highest test accuracy)
best_dropout_index = np.argmax([max(acc[1]) for acc in accuracies])
best_dropout = dropout_values[best_dropout_index]
best_model = models[best_dropout_index]

print(f"Best Dropout Value: {best_dropout}")