Pass additional parameter in call function of custom keras layer

I created a custom keras layer with the purpose of manually changing activations of previous layer during inference. Following is basic layer that simply multiplies the activations with a number.

import numpy as np
from keras import backend as K
from keras.layers import Layer
import tensorflow as tf

class myLayer(Layer):

    def __init__(self, n=None, **kwargs):
        self.n = n
        super(myLayer, self).__init__(**kwargs)

    def build(self, input_shape):

        self.output_dim = input_shape[1]
        super(myLayer, self).build(input_shape)

    def call(self, inputs):

        changed = tf.multiply(inputs, self.n)

        forTest  = changed
        forTrain = inputs

        return K.in_train_phase(forTrain, forTest)

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.output_dim)

It works fine when I use it like this with IRIS dataset

model = Sequential()
model.add(Dense(units, input_shape=(5,)))
model.add(Activation('relu'))
model.add(myLayer(n=3))
model.add(Dense(units))
model.add(Activation('relu'))
model.add(Dense(3))
model.add(Activation('softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
model.summary()

However now I want to move 'n' from init to the call function so I can apply different values of n after training to evaluate model. The idea is to have a placeholder inplace of n which can be initialzed with some value before calling the evaluate function on it. I am not sure how to achieve this. What would the correct approach for this? Thanks