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