Stacking classifiers (sklearn and keras models) via StackingCVClassifier problem

[ghost]

Hello. I am kind of new to using the mlxtend package and as well as the Keras package so please bear with me. I have been trying to combine predictions of various models, i.e., Random Forest, Logistic Regression, and a Neural Network model, using StackingCVClassifier. I am trying to stack these classifiers that operate on different feature subsets. Kindly see the code as follows:

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from tensorflow import keras
from keras import layers
from keras.constraints import maxnorm
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation,  Flatten, Input
from mlxtend.classifier import StackingCVClassifier
from mlxtend.feature_selection import ColumnSelector
from sklearn.pipeline import make_pipeline
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.neural_network import MLPClassifier

X, y = make_classification()
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=0)

# defining neural network model
def create_model ():
    # create model
    model = Sequential()
    model.add(Dense(10, input_dim=10, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Flatten())
    optimizer= keras.optimizers.RMSprop(lr=0.001)
    model.add(Dense(units = 1, activation = 'sigmoid'))  # Compile model
    model.compile(loss='binary_crossentropy',
                  optimizer=optimizer, metrics=[keras.metrics.AUC(), 'accuracy'])
    return model

# using KerasClassifier on the neural network model
NN_clf=KerasClassifier(build_fn=create_model, epochs=5, batch_size= 5)
NN_clf._estimator_type = "classifier"

# stacking of classifiers that operate on different feature subsets
pipeline1 = make_pipeline(ColumnSelector(cols=(np.arange(0, 5, 1))), LogisticRegression())
pipeline2 = make_pipeline(ColumnSelector(cols=(np.arange(5, 10, 1))), RandomForestClassifier())
pipeline3 = make_pipeline(ColumnSelector(cols=(np.arange(10, 20, 1))), NN_clf)

# final stacking
clf = StackingCVClassifier(classifiers=[pipeline1, pipeline2, pipeline3], meta_classifier=MLPClassifier())
clf.fit(X_train, y_train)

print("Stacking model score: %.3f" % clf.score(X_val, y_val))

However, I am getting this error:

ValueError                                Traceback (most recent call last)
<ipython-input-11-ef342536824f> in <module>
     42 # final stacking
     43 clf = StackingCVClassifier(classifiers=[pipeline1, pipeline2, pipeline3], meta_classifier=MLPClassifier())
---> 44 clf.fit(X_train, y_train)
     45 
     46 print("Stacking model score: %.3f" % clf.score(X_val, y_val))

~\anaconda3\lib\site-packages\mlxtend\classifier\stacking_cv_classification.py in fit(self, X, y, groups, sample_weight)
    282                 meta_features = prediction
    283             else:
--> 284                 meta_features = np.column_stack((meta_features, prediction))
    285 
    286         if self.store_train_meta_features:

~\anaconda3\lib\site-packages\numpy\core\overrides.py in column_stack(*args, **kwargs)

~\anaconda3\lib\site-packages\numpy\lib\shape_base.py in column_stack(tup)
    654             arr = array(arr, copy=False, subok=True, ndmin=2).T
    655         arrays.append(arr)
--> 656     return _nx.concatenate(arrays, 1)
    657 
    658 

~\anaconda3\lib\site-packages\numpy\core\overrides.py in concatenate(*args, **kwargs)

ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 3 dimension(s)

I am currently working on a project that combines predictions from different classifiers and this is how I thought I would tackle it. I would appreciate your help so much. Thank you so much!