code: def __init__(self, model_config: AttrDict, model_name: str):
      super(ResNetSelectiveKernel, self).__init__()
      self.model_config = model_config
  
      self.trunk_config = self.model_config.TRUNK.RESNETS_SK
      self.depth = SUPPORTED_DEPTHS(self.trunk_config.DEPTH)
      self.width_multiplier = self.trunk_config.WIDTH_MULTIPLIER
      self.sk_ratio = self.trunk_config.SK_RATIO
  
      layers = BLOCK_CONFIG[self.depth]
      width_multiplier = self.width_multiplier
      sk_ratio = self.sk_ratio
  
      logging.info(
          f"Building model: ResNet-SK"
          f"-d{self.depth}-{self.width_multiplier}x"
          f"-sk{self.sk_ratio}"
      )
  
      ops = [Stem(sk_ratio, width_multiplier)]
      channels_in = 64 * width_multiplier
      ops.append(
          Blocks(
              layers[0], channels_in, 64 * width_multiplier, 1, sk_ratio
          )
      )
      channels_in = ops[-1].channels_out
      ops.append(
          Blocks(
              layers[1],
              channels_in,
              128 * width_multiplier,
              2,
              sk_ratio,
          )
      )
      channels_in = ops[-1].channels_out
      ops.append(
          Blocks(
              layers[2],
              channels_in,
              256 * width_multiplier,
              2,
              sk_ratio,
          )
      )
      channels_in = ops[-1].channels_out
      ops.append(
          Blocks(
              layers[3],
              channels_in,
              512 * width_multiplier,
              2,
              sk_ratio,
          )
      )
      channels_in = ops[-1].channels_out
      self.channels_out = channels_in
      self.net = nn.Sequential(*ops)
  
imports: import logging
  from enum import Enum
  import torch
  import torch.nn
  import torch.nn.functional
  from vissl.config import AttrDict
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 1 to create a new ResNetSelectiveKernel object from inherited base classes: nn with example: obj = ResNetSelectiveKernel(model_config, model_name)