What is the from repeating same layers in AI architecture

Question

I know that in ai models lstm is used to extract features from data and dropout is used to focus in the main ones, but i can't understand why people used to repeat the layers multiple times like those 3 layers (lstm dropout Batchnormalization) are repeated 3 times so what if i just put lstm dropout Batchnormalization and then dense??? Can any one explain what's the benefits of repeating them while the same features will be extracted as i think.

 def buildModel(self,x_train):
    model = Sequential()
    x = len(x_train[0])

    model.add(LSTM(256,input_shape=((1,x)), return_sequences = True, activation = "relu"))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())

    model.add(LSTM(128, input_shape=((1, x)), return_sequences=True, activation="relu"))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())

    model.add(LSTM(128, input_shape=((1, x)), activation="relu"))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())

    model.add(Dense(32, activation="relu"))
    model.add(Dropout(0.2))

    model.add(Dense(2, activation="softmax"))
    optimizer = tf.keras.optimizers.Adam(lr = 0.001, decay = 1e-6)
    model.compile(loss="sparse_categorical_crossentropy",optimizer=optimizer)

    return model