code: def __init__(
      self,
      stem,
      blocks_args,
      head,
      dropout_rate,
      num_classes=1000,
      **kwargs,
  ):
      super().__init__()
  
      self.stem = nn.Sequential(
          nn.Conv2d(3, stem, 3, 2, 1, bias=False),
          nn.BatchNorm2d(stem),
          nn.ReLU(True),
      )
  
      self.blocks = nn.Sequential(
          *[MixModule(*args) for args in blocks_args]
      )
  
      self.classifier = nn.Sequential(
          nn.Conv2d(blocks_args[-1].out_channels, head, 1, bias=False),
          nn.BatchNorm2d(head),
          nn.ReLU(True),
          nn.AdaptiveAvgPool2d(1),
          Flatten(),
          nn.Dropout(dropout_rate),
          nn.Linear(head, num_classes),
      )
  
      self.init_weights()
  
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 1 to create a new MixNet object from inherited base classes: nn with example: obj = MixNet(stem, blocks_args, head, dropout_rate, num_classes)