If I understood correctly, you want to incorporate the batch norm parameters into e.g. a convolution,  then you can do e.g. this:
    def incorporateBatchNorm(self, bn):

        gamma = bn.weight
        beta = bn.bias
        mean = bn.running_mean
        var = bn.running_var
        eps = bn.eps

        var_sqrt = torch.sqrt(var + eps)

        w = (self.weight * gamma.reshape(self.out_channels, 1, 1, 1)) / var_sqrt.reshape(self.out_channels, 1,
                                                                                         1, 1)
        b = ((self.bias - mean) * gamma) / var_sqrt + beta

        self.weight = nn.Parameter(w)
        self.bias = nn.Parameter(b)

And then you can just do the forward pass of the convolution layer.