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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 get_rmse_log(net, X_train, y_train):
 """Gets root mse between the logarithms of the prediction and the truth."""

    num_train = X_train.shape[0]
    clipped_preds = nd.clip(net(X_train), 1, float('inf'))
    return np.sqrt(2 * nd.sum(square_loss(
        nd.log(clipped_preds), nd.log(y_train))).asscalar() / num_train)