Why does batch norm layer has the parameter of weight and bias?

[SanftMonster]

In this tutorial, when explaining the weight file, it mentioned that if a convolutional layer has batch norm, then it will get 4 parameters from the weight file. The 4 parameters are: bn_weight, bn_bias, bn_running_mean, bn_running_var. But for batch normalization, why do we still need bn_weight and bn_bias? What kind of role do the bn_weight and bn_bias cast?
I am a freshman of DL so my question may seem to be dull...I will appreciate it if you could tell me the answer or where to find the answer.

[bot66]

just google batch normal definition, batch normal having learnable parameters , not just simply normalize the input data, every epoch will update its parameters.

[SanftMonster]

just google batch normal definition, batch normal having learnable parameters , not just simply normalize the input data, every epoch will update its parameters.

Thank you very much!