code: def forward(self, x):
      # print("Input : ", x.shape)
      stem = self.stem(x)
      # print("Stem : ", x.shape)
      feature = self.blocks(stem)
      # print("feature : ", feature.shape)
      out = self.classifier(feature)
      return out
  
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 3 for calling the MixNet obj's underlying member method code with example usage: obj.forward(x) and returns: out