code: def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
                         learning_rate, weight_decay, batch_size):
      
      assert k > 1
      fold_size = X_train.shape[0] // k
  
      train_loss_sum = 0.0
      test_loss_sum = 0.0
      for test_idx in range(k):
          X_val_test = X_train[test_idx * fold_size: (test_idx + 1) *
                                                     fold_size, :]
          y_val_test = y_train[test_idx * fold_size: (test_idx + 1) * fold_size]
          val_train_defined = False
          for i in range(k):
              if i != test_idx:
                  X_cur_fold = X_train[i * fold_size: (i + 1) * fold_size, :]
                  y_cur_fold = y_train[i * fold_size: (i + 1) * fold_size]
                  if not val_train_defined:
                      X_val_train = X_cur_fold
                      y_val_train = y_cur_fold
                      val_train_defined = True
                  else:
                      X_val_train = nd.concat(X_val_train, X_cur_fold, dim=0)
                      y_val_train = nd.concat(y_val_train, y_cur_fold, dim=0)
          net = get_net()
          train_loss = train(net, X_val_train, y_val_train, epochs, verbose_epoch,
                             learning_rate, weight_decay, batch_size)
          train_loss_sum += train_loss
          test_loss = get_rmse_log(net, X_val_test, y_val_test)
          print("Test loss: %f" % test_loss)
          test_loss_sum += test_loss
      return train_loss_sum / k, test_loss_sum / k
docs: Conducts k-fold cross validation for the model.