code: def __init__(
      self, in_features, out_features, bias=True, std=0.02, drop=0.0
  ):
      super().__init__()
      self.bn = nn.BatchNorm1d(in_features)
      self.drop = nn.Dropout(drop)
      self.linear = nn.Linear(in_features, out_features, bias=bias)
  
      trunc_normal_(self.linear.weight, std=std)
      if self.linear.bias is not None:
          nn.init.constant_(self.linear.bias, 0)
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new NormLinear object from inherited base classes: torch.nn with example: obj = NormLinear(in_features, out_features, bias, std, drop)