def METHOD_NAME(self):
    for m in self.modules():
        if isinstance(m, Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))
            if m.bias is not None:
                m.bias.data.zero_()
        elif isinstance(m, BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, Linear):
            n = m.weight.size(1)
            m.weight.data.normal_(0, 0.01)
            m.bias.data.zero_()