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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def transform(self, X, y=None):
 """Transform data by adding two virtual features. Parameters X: numpy ndarray, {n_samples, n_components} New data, where n_samples is the number of samples and n_components is the number of components. y: None Unused Returns ------- X_transformed: array-like, shape (n_samples, n_features) The transformed feature set """

        X = check_array(X)
        n_features = X.shape[1]

        X_transformed = np.copy(X)

        non_zero_vector = np.count_nonzero(X_transformed, axis=1)
        non_zero = np.reshape(non_zero_vector, (-1, 1))
        zero_col = np.reshape(n_features - non_zero_vector, (-1, 1))

        X_transformed = np.hstack((non_zero, X_transformed))
        X_transformed = np.hstack((zero_col, X_transformed))

        return X_transformed