code: def __init__(self, pretrained=True, layers=None, activation=ACT_RELU):
      if layers is None:
          layers = [1, 2, 3, 4]
      from timm.models import skresnext50_32x4d
  
      act_layer = get_activation_block(activation)
      encoder = skresnext50_32x4d(
          pretrained=pretrained, act_layer=act_layer
      )
      super().__init__(
          [64, 256, 512, 1024, 2048], [2, 4, 8, 16, 32], layers
      )
      self.stem = nn.Sequential(
          OrderedDict(
              [
                  ("conv1", encoder.conv1),
                  ("bn1", encoder.bn1),
                  ("act1", encoder.act1),
              ]
          )
      )
  
      self.layer1 = nn.Sequential(encoder.maxpool, encoder.layer1)
      self.layer2 = encoder.layer2
      self.layer3 = encoder.layer3
      self.layer4 = encoder.layer4
  
imports: from collections import OrderedDict
  from typing import List
  import torch
  from torch import nn
  from .common import GenericTimmEncoder
  from ..common import EncoderModule, make_n_channel_input
  from ...None import ACT_RELU, get_activation_block
label_desc: Use image node_id 1 to create a new SKResNeXt50Encoder object from inherited base classes: EncoderModule with example: obj = SKResNeXt50Encoder(pretrained, layers, activation)