<!-- BEGIN COPY / PASTE -->
    from sklearn.model_selection import GridSearchCV
    from sklearn.neural_network import MLPClassifier

    # Define the model
    model = MLPClassifier()

    # Define the hyperparameters and their possible values
    param_grid = {
        'hidden_layer_sizes': [(50,), (100,), (50, 50)],
        'activation': ['tanh', 'relu'],
        'solver': ['sgd', 'adam'],
        'alpha': [0.0001, 0.05],
        'learning_rate': ['constant', 'adaptive'],
    }

    # Set up the grid search
    grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)

    # Fit the grid search to the data
    grid_search.fit(X_train, y_train)

    # Best parameters found
    print(grid_search.best_params_)
    <!-- END COPY / PASTE -->