text: from ._base import EncoderMixin
  from timm.models.resnet import ResNet
  from timm.models.sknet import SelectiveKernelBottleneck, SelectiveKernelBasic
  import torch.nn as nn
  
  
  class SkNetEncoder(ResNet, EncoderMixin):
      def __init__(self, out_channels, depth=5, **kwargs):
          super().__init__(**kwargs)
          self._depth = depth
          self._out_channels = out_channels
          self._in_channels = 3
  
          del self.fc
          del self.global_pool
  
      def get_stages(self):
          return [
              nn.Identity(),
              nn.Sequential(self.conv1, self.bn1, self.act1),
              nn.Sequential(self.maxpool, self.layer1),
              self.layer2,
              self.layer3,
              self.layer4,
          ]
  
      def forward(self, x):
          stages = self.get_stages()
  
          features = []
          for i in range(self._depth + 1):
              x = stages[i](x)
              features.append(x)
  
          return features
  
      def load_state_dict(self, state_dict, **kwargs):
          state_dict.pop("fc.bias", None)
          state_dict.pop("fc.weight", None)
          super().load_state_dict(state_dict, **kwargs)
  
  
  sknet_weights = {
      "timm-skresnet18": {
          "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet18_ra-4eec2804.pth",  # noqa
      },
      "timm-skresnet34": {
          "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet34_ra-bdc0ccde.pth",  # noqa
      },
      "timm-skresnext50_32x4d": {
          "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth",  # noqa
      },
  }
  
  pretrained_settings = {}
  for model_name, sources in sknet_weights.items():
      pretrained_settings[model_name] = {}
      for source_name, source_url in sources.items():
          pretrained_settings[model_name][source_name] = {
              "url": source_url,
              "input_size": [3, 224, 224],
              "input_range": [0, 1],
              "mean": [0.485, 0.456, 0.406],
              "std": [0.229, 0.224, 0.225],
              "num_classes": 1000,
          }
  
  timm_sknet_encoders = {
      "timm-skresnet18": {
          "encoder": SkNetEncoder,
          "pretrained_settings": pretrained_settings["timm-skresnet18"],
          "params": {
              "out_channels": (3, 64, 64, 128, 256, 512),
              "block": SelectiveKernelBasic,
              "layers": [2, 2, 2, 2],
              "zero_init_last_bn": False,
              "block_args": {"sk_kwargs": {"rd_ratio": 1 / 8, "split_input": True}},
          },
      },
      "timm-skresnet34": {
          "encoder": SkNetEncoder,
          "pretrained_settings": pretrained_settings["timm-skresnet34"],
          "params": {
              "out_channels": (3, 64, 64, 128, 256, 512),
              "block": SelectiveKernelBasic,
              "layers": [3, 4, 6, 3],
              "zero_init_last_bn": False,
              "block_args": {"sk_kwargs": {"rd_ratio": 1 / 8, "split_input": True}},
          },
      },
      "timm-skresnext50_32x4d": {
          "encoder": SkNetEncoder,
          "pretrained_settings": pretrained_settings["timm-skresnext50_32x4d"],
          "params": {
              "out_channels": (3, 64, 256, 512, 1024, 2048),
              "block": SelectiveKernelBottleneck,
              "layers": [3, 4, 6, 3],
              "zero_init_last_bn": False,
              "cardinality": 32,
              "base_width": 4,
          },
      },
  }
  