Keras parallel layers merging - keras

Question

I read Keras documentation, but had not found any explanation on the following error
Code:
import numpy as np
import pandas as pd
from tensorflow.keras import layers
from keras.optimizers import SGD
from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.layers.core import Dense, Activation, Dropout, Reshape, Flatten
from keras.utils.np_utils import to_categorical
data = pd.read_excel('oildata.xlsx')
firstBranch = Sequential()
#firstBranch.add(Reshape((1,28,28), input_shape=(,)))
firstBranch.add(LSTM(64, input_shape=(10, 1100)))
#firstBranch.add(MaxPooling2D((2, 2), strides=(2, 2)))
firstBranch.add(Flatten())
secondBranch = Sequential()
secondBranch.add(BatchNormalization(name = 'batch_norm_0', input_shape = (1000, 10, 1, 1)))
secondBranch.add(ConvLSTM2D(name ='conv_lstm_1',
filters = 64, kernel_size = (10, 1),
padding = 'same',
return_sequences = False))
secondBranch.add(Dropout(0.10, name = 'dropout_1'))
secondBranch.add(BatchNormalization(name = 'batch_norm_1'))
# model.add(ConvLSTM2D(name ='conv_lstm_2',
# filters = 64, kernel_size = (5, 1),
# padding='same',
# return_sequences = False))
# model.add(Dropout(0.20, name = 'dropout_2'))
# model.add(BatchNormalization(name = 'batch_norm_2'))
secondBranch.add(Flatten())
secondBranch.add(RepeatVector(1000))
secondBranch.add(Reshape((1000, 10, 1, 64)))
# model.add(ConvLSTM2D(name ='conv_lstm_3',
# filters = 64, kernel_size = (10, 1),
# padding='same',
# return_sequences = True))
# model.add(Dropout(0.20, name = 'dropout_3'))
# model.add(BatchNormalization(name = 'batch_norm_3'))
secondBranch.add(ConvLSTM2D(name ='conv_lstm_4',
filters = 64, kernel_size = (5, 1),
padding='same',
return_sequences = True))
secondBranch.add(TimeDistributed(Dense(units=1, name = 'dense_1', activation = 'relu')))
secondBranch.add(Dense(units=1, name = 'dense_2'))
secondBranch.add(Flatten())
thirdBranch = Sequential()
thirdBranch.add(Reshape((1,28,28), input_shape=(784,)))
thirdBranch.add(Dense(10, activation='relu'))
thirdBranch.add(Flatten())
fourthBranch = Sequential()
#fourthBranch.add(Reshape((1,28,28), input_shape=(784,)))
fourthBranch.add(Dense(10, activation='relu'))
fourthBranch.add(Flatten())
#merged = Concatenate([firstBranch, secondBranch, thirdBranch,fourthBranch], mode='concat')
merged = Concatenate([firstBranch,secondBranch,thirdBranch,fourthBranch])
model = Sequential()
model.add(merged)
model.add(Dense(28*3, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(28, activation='relu'))
model.add(Dense(19))
model.add(Activation("softmax"))
sgd = SGD(lr=0.5, momentum=0.8, decay=0.1, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit([X,X,X,X],X, batch_size=100, verbose=2)
yPred = model.predict([X,X,X,X],X)
Error:
TypeError Traceback (most recent call last)
<ipython-input-385-11a86cc54884> in <module>
88 sgd = SGD(lr=0.5, momentum=0.0, decay=0.0, nesterov=False)
89 model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
---> 90 model.fit([X,X,X,X],X, batch_size=100, verbose=2)
91
92 yPred = model.predict([X,X,X,X],X)
...........................................
TypeError: list indices must be integers or slices, not ListWrapper"
What does it mean ListWrapper? Data is turned into frames and had to fit the model.