code: def init_weights(self):
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
          elif isinstance(m, nn.Linear):
              nn.init.kaiming_uniform_(
                  m.weight, mode="fan_in", nonlinearity="linear"
              )
  
imports: import torch.nn
  from .layers import Flatten
  from .layers import SEModule
  from .layers import Swish
  from .mdconv import MDConv
  from .utils import MixnetDecoder
  from .utils import round_filters
label_desc: Use image node_id 2 for calling the MixNet obj's underlying member method code with example usage: obj.init_weights() without return types