Need to convert lasagne to keras code (CNNLSTM)

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Python

tensorflow

deep-learning

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I spent a lot of time converting Lasagne's code to TensorFlow Keras. Although I successfully ran the code, however, I could not obtain the result reported in the paper.
Some help is available on python - convert Lasagne to Keras code (CNN -> LSTM) - Stack Overflow, but it is limited to specific layers and not the entire architecture.

Lasagne Cod

net = {}
    net['input'] = lasagne.layers.InputLayer((BATCH_SIZE, 1, SLIDING_WINDOW_LENGTH, NB_SENSOR_CHANNELS))
    net['conv1/5x1'] = lasagne.layers.Conv2DLayer(net['input'], NUM_FILTERS, (FILTER_SIZE, 1))
    net['conv2/5x1'] = lasagne.layers.Conv2DLayer(net['conv1/5x1'], NUM_FILTERS, (FILTER_SIZE, 1))
    net['conv3/5x1'] = lasagne.layers.Conv2DLayer(net['conv2/5x1'], NUM_FILTERS, (FILTER_SIZE, 1))
    net['conv4/5x1'] = lasagne.layers.Conv2DLayer(net['conv3/5x1'], NUM_FILTERS, (FILTER_SIZE, 1))
    net['shuff'] = lasagne.layers.DimshuffleLayer(net['conv4/5x1'], (0, 2, 1, 3))
    net['lstm1'] = lasagne.layers.LSTMLayer(net['shuff'], NUM_UNITS_LSTM)
    net['lstm2'] = lasagne.layers.LSTMLayer(net['lstm1'], NUM_UNITS_LSTM)
    # In order to connect a recurrent layer to a dense layer, it is necessary to flatten the first two dimensions
    # to cause each time step of each sequence to be processed independently (see Lasagne docs for further information)
    net['shp1'] = lasagne.layers.ReshapeLayer(net['lstm2'], (-1, NUM_UNITS_LSTM))
    net['prob'] = lasagne.layers.DenseLayer(net['shp1'],NUM_CLASSES, nonlinearity=lasagne.nonlinearities.softmax)
    # Tensors reshaped back to the original shape
    net['shp2'] = lasagne.layers.ReshapeLayer(net['prob'], (BATCH_SIZE, FINAL_SEQUENCE_LENGTH, NUM_CLASSES))
    # Last sample in the sequence is considered
    net['output'] = lasagne.layers.SliceLayer(net['shp2'], -1, 1)

Problem

I believe that all of the lines I converted are correct, except lst two lines of Lasagne code, which are as follows:

# Tensors reshaped back to the original shape
    net['shp2'] = lasagne.layers.ReshapeLayer(net['prob'], (BATCH_SIZE, FINAL_SEQUENCE_LENGTH, NUM_CLASSES))
    # Last sample in the sequence is considered
    net['output'] = lasagne.layers.SliceLayer(net['shp2'], -1, 1)

I'm not sure how to convert these lines in Tensorflow Keras after my output_layer, or what these lines are for.

What I have tried:

I come up with this in TensorFlow Keras

def CNN_model(input_shape, total_classes):   # input_shape = (1, 30, 52) total_classes=12
        input_layer = tf.keras.Input(shape=input_shape, name="Time_Series_Activity")  #Tensor("Time_Series_Activity:0", shape=(None, 1, 30, 52), dtype=float32)
        con_l1 = tf.keras.layers.Conv2D(64, (5, 1), activation="relu", data_format='channels_first')(input_layer) # Tensor("conv2d/Relu:0", shape=(None, 64, 26, 52), dtype=float32)
        con_l2 = tf.keras.layers.Conv2D(64, (5, 1), activation="relu", data_format='channels_first')(con_l1) #Tensor("conv2d_1/Relu:0", shape=(None, 64, 22, 52), dtype=float32)
        con_l3 = tf.keras.layers.Conv2D(64, (5, 1), activation="relu", data_format='channels_first')(con_l2) # Tensor("conv2d_2/Relu:0", shape=(None, 64, 18, 52), dtype=float32)
        con_l4 = tf.keras.layers.Conv2D(64, (5, 1), activation="relu", data_format='channels_first')(con_l3) # Tensor("conv2d_3/Relu:0", shape=(None, 64, 14, 52), dtype=float32)
 
        
        permute_layer = tf.keras.layers.Permute((2, 1, 3))(con_l4) # Tensor("permute/transpose:0", shape=(None, 14, 64, 52), dtype=float32)
        rl = Reshape((int(permute_layer.shape[1]), int(permute_layer.shape[2]) * int(permute_layer.shape[3])))(permute_layer) # Tensor("reshape/Reshape:0", shape=(None, 14, 3328), dtype=float32)


        lstm_l5 = tf.keras.layers.LSTM(128, return_sequences=True, dropout=0.5)(rl) # Tensor("lstm/PartitionedCall:1", shape=(None, 14, 128), dtype=float32)
        lstm_l6 = tf.keras.layers.LSTM(128, dropout=0.5)(lstm_l5) # Tensor("lstm_1/PartitionedCall:0", shape=(None, 128), dtype=float32)

        output_layer = tf.keras.layers.Dense(total_classes, activation="softmax")(lstm_l6) # Tensor("dense/Softmax:0", shape=(None, 12), dtype=float32)

        return tf.keras.models.Model(inputs=input_layer, outputs=output_layer)