class_docstr: SelecSls42 / SelecSls60 / SelecSls84
  
  Parameters
  ----------
  cfg : network config dictionary specifying block type, feature, and head args
  num_classes : int, default 1000
      Number of classification classes.
  in_chans : int, default 3
      Number of input (color) channels.
  drop_rate : float, default 0.
      Dropout probability before classifier, for training
  global_pool : str, default 'avg'
      Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
code: def __init__(
      self,
      cfg,
      num_classes=1000,
      in_chans=3,
      drop_rate=0.0,
      global_pool="avg",
  ):
      self.num_classes = num_classes
      super(SelecSls, self).__init__()
  
      self.stem = conv_bn(in_chans, 32, stride=2)
      self.features = SequentialList(
          *[cfg["block"](*block_args) for block_args in cfg["features"]]
      )
      self.from_seq = (
          SelectSeq()
      )  # from List[tensor] -> Tensor in module compatible way
      self.head = nn.Sequential(
          *[conv_bn(*conv_args) for conv_args in cfg["head"]]
      )
      self.num_features = cfg["num_features"]
      self.feature_info = cfg["feature_info"]
  
      self.global_pool, self.head_drop, self.fc = create_classifier(
          self.num_features,
          self.num_classes,
          pool_type=global_pool,
          drop_rate=drop_rate,
      )
  
      for n, m in self.named_modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
  
imports: from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new SelecSls object from inherited base classes: nn with example: obj = SelecSls(cfg, num_classes, in_chans, drop_rate, global_pool)