Batch Normalization does not need batch size. The learnable of batch normalization are of shape 2xC where C is the channel dimension of a 3D input (NxCxL) and the 2 is for the mean and variance at each channel. If the input is 2D then the learnable parameters of batch norm will be of shape (2x1).
Batch Norm will compute the mean and variance across N where N is the batch size so it is an unimportant parameter for batch normalization.