code: def __init__(
      self,
      in_chs,
      out_chs,
      stride,
      dilation=(1, 1),
      exp_ratio=1.0,
      se_ratio=0.0,
      ch_div=1,
      act_layer="swish",
      dw_act_layer="relu6",
      drop_path=None,
  ):
      super(LinearBottleneck, self).__init__()
      self.use_shortcut = (
          stride == 1
          and dilation[0] == dilation[1]
          and in_chs <= out_chs
      )
      self.in_channels = in_chs
      self.out_channels = out_chs
  
      if exp_ratio != 1.0:
          dw_chs = make_divisible(
              round(in_chs * exp_ratio), divisor=ch_div
          )
          self.conv_exp = ConvNormAct(
              in_chs, dw_chs, act_layer=act_layer
          )
      else:
          dw_chs = in_chs
          self.conv_exp = None
  
      self.conv_dw = ConvNormAct(
          dw_chs,
          dw_chs,
          kernel_size=3,
          stride=stride,
          dilation=dilation[0],
          groups=dw_chs,
          apply_act=False,
      )
      if se_ratio > 0:
          self.se = SEWithNorm(
              dw_chs,
              rd_channels=make_divisible(
                  int(dw_chs * se_ratio), ch_div
              ),
          )
      else:
          self.se = None
      self.act_dw = create_act_layer(dw_act_layer)
  
      self.conv_pwl = ConvNormAct(dw_chs, out_chs, 1, apply_act=False)
      self.drop_path = drop_path
  
imports: from functools import partial
  from math import ceil
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import ClassifierHead, create_act_layer, ConvNormAct, DropPath, make_divisible, SEModule
  from ._builder import build_model_with_cfg
  from ._efficientnet_builder import efficientnet_init_weights
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 1 to create a new LinearBottleneck object from inherited base classes: nn with example: obj = LinearBottleneck(in_chs, out_chs, stride, dilation, exp_ratio, se_ratio, ch_div, act_layer, dw_act_layer, drop_path)