Incompatible input shape - tensorflow

Question

I have a CNN-LSTM model where the CNN model takes as input data with shape (None, 301,4,1) and outputs data with shape (None, 606). To adapt the cnn output to the input of the LSTM, I added a TimeDistributed Layer where it call the CNN model each window-size=100, so the input shape of this layer=(None, 100,301,4,1), and then we have some stacked LSTM layers.
This is the architecture of the CNN model:
This is the architecture of the LSTM model:
The code for this architecture is the following :
input_layer1=Input(shape=(301,4,1))
...
merge_layer=Concatenate(axis=1)([global_max_pooling, lambda_14])
cnn_model = Model(inputs= input_layer1, outputs=merge_layer) cnn_model.compile(optimizer=RMSprop(),loss="mean_squared_error",metrics=['mse', 'mae'])
input_lstm = Input(shape=(100,301,4,1))
cnn_output = TimeDistributed(cnn_model)(input_lstm)
...
output_layer=Dense(1,activation="linear")(lstm3)
cnn_lstm_model = Model(inputs= input_lstm, outputs=output_layer)
cnn_lstm_model.compile(optimizer=RMSprop(),loss="mean_squared_error",metrics=['mse', 'mae'])
Then saved only the cnn_lstm_model model.
For the training, this is my code:
batchsize=100
epoch=20
cnn_lstm_model.fit(train_data_force_temp_X,data_Y,
batch_size=batchsize,
epochs=epoch,
verbose=1,
shuffle=True,
validation_data=(test_data_force_temp_X,test_Y),
callbacks=[TensorBoard(log_dir="./CNN_LSTM")])
Where train_data_force_temp_X.shape = (1960, 301, 4, 1) , PS: 1960 is number of samples.
But I have this issue :
ValueError: Input 0 is incompatible with layer model_1: expected
shape=(None, 100, 301, 4, 1), found shape=(None, 301, 4, 1)
I understood that passed the wrong shape to the cnn_lstm_model but I thought that it will pass the data first to the cnn model which have the shape=(None, 301, 4, 1) and then for each 100 CNN outputs it will call the time distributed layer and continue the process, It seems I have not understood the process correctly.
So my question is :
do I have to run the data first into the cnn model, do prediction and
then used those outputs as an input for the cnn_lstm model ?
How can I fix the training process ?
Thank you in advance for the help.