How to apply the ConvLSTM layers

I was doing a classification machine learning with an input of (700,50,34) (batch, step,features)

def convLSTM_model(X_train, y_train, X_test, y_test, num_classes,loss, batch_size=68, units=128, learning_rate=0.005,
                           epochs=20, dropout=0.2, recurrent_dropout=0.2):
    class myCallback(tf.keras.callbacks.Callback):

        def on_epoch_end(self, epoch, logs={}):
            if (logs.get('acc') > 0.9):
                print("\nReached 90% accuracy so cancelling training!")
                self.model.stop_training = True

    callbacks = myCallback()

    model = tf.keras.models.Sequential()
    model.add(Masking(mask_value=0.0, input_shape=(None,X_train.shape[0],X_train.shape[1], X_train.shape[2])))
    
    model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3), padding="same", return_sequences=True))
    model.add(BatchNormalization())

    model.add(Bidirectional(LSTM(units, dropout=dropout, recurrent_dropout=recurrent_dropout, return_sequences=True)))
    model.add(Bidirectional(LSTM(units, dropout=dropout, recurrent_dropout=recurrent_dropout, return_sequences=True)))
    model.add(Bidirectional(LSTM(units, dropout=dropout, recurrent_dropout=recurrent_dropout)))
    model.add(Dense(30, activation='relu'))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))

    adamopt = tf.keras.optimizers.Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-8)

    model.compile(loss=loss,
                  optimizer=adamopt,
                  metrics=['accuracy'])

    history = model.fit(X_train, y_train,
                        batch_size=batch_size,
                        epochs=epochs,
                        validation_data=(X_test, y_test),
                        verbose=1,
                        callbacks=[callbacks])

    score, acc = model.evaluate(X_test, y_test,
                                batch_size=batch_size)

    yhat = model.predict(X_test)

    return history, that

Apparently, changing the input_shape and simply adding

model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3), padding="same", return_sequences=True))
model.add(BatchNormalization())

does not work.

ValueError: Dimension 1 in both shapes must be equal, but are 708 and 501264. Shapes are [?,708,50,40] and [?,501264,2500,40]. for 'conv_lst_m2d/while/Select' (op: 'Select') with input shapes: [?,501264,2500,40], [?,708,50,40], [?,708,50,40].

How should I approach? Is there any suggestion on the number of filter?

from How to apply the ConvLSTM layers