Do you want to use the parameters from another BatchNorm layer and just add another layer on top of it or do you want to rewrite it completely?
In the former case you could try something like this:
class MyBatchNorm(nn.Module):
    def __init__(self, num_features, eps=1e-05, momentum=0.1, affine=True):
        super(MyBatchNorm, self).__init__()
        self.bn = nn.BatchNorm1d(num_features,
                                 eps=eps,
                                 momentum=momentum,
                                 affine=affine)

    def forward(self, x):        
        x = self.bn(x)
        mu = self.bn.running_mean
        var = self.bn.running_var
        gamma = self.bn.weight
        beta = self.bn.bias
        eps = self.bn.eps    
        k = gamma.data / torch.sqrt(var + eps)
        x.data = k * x.data + beta.data
        return x

mybn = MyBatchNorm(10)
x = Variable(torch.randn(16, 10))
x_ = mybn(x)

Note that I calculated mu, var, ... separately just to show how to get them. Of course you can simplify the code and just call e.g. self.bn.weight for your calculations.
Let me know, if this meets your need or if I misunderstood your question.