def METHOD_NAME(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.xavier_uniform_(m.weight.data)
            if m.bias is not None:
                m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm1d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.Linear):
            nn.init.xavier_uniform_(m.weight.data)
            if m.bias is not None:
                m.bias.data.zero_()