code: def __init__(
      self,
      kernel_size: int,
      input_c: int,
      out_c: int,
      expand_ratio: int,
      stride: int,
      se_ratio: float,
      drop_rate: float,
      norm_layer: Callable[..., nn.Module],
  ):
      super(FusedMBConv, self).__init__()
  
      assert stride in [1, 2]
      assert se_ratio == 0
  
      self.has_shortcut = stride == 1 and input_c == out_c
      self.drop_rate = drop_rate
  
      self.has_expansion = expand_ratio != 1
  
      activation_layer = nn.SiLU  # alias Swish
      expanded_c = input_c * expand_ratio
  
      # 只有当expand ratio不等于1时才有expand conv
      if self.has_expansion:
          # Expansion convolution
          self.expand_conv = ConvBNAct(
              input_c,
              expanded_c,
              kernel_size=kernel_size,
              stride=stride,
              norm_layer=norm_layer,
              activation_layer=activation_layer,
          )
  
          self.project_conv = ConvBNAct(
              expanded_c,
              out_c,
              kernel_size=1,
              norm_layer=norm_layer,
              activation_layer=nn.Identity,
          )  # 注意没有激活函数
      else:
          # 当只有project_conv时的情况
          self.project_conv = ConvBNAct(
              input_c,
              out_c,
              kernel_size=kernel_size,
              stride=stride,
              norm_layer=norm_layer,
              activation_layer=activation_layer,
          )  # 注意有激活函数
  
      self.out_channels = out_c
  
      # 只有在使用shortcut连接时才使用dropout层
      self.drop_rate = drop_rate
      if self.has_shortcut and drop_rate > 0:
          self.dropout = DropPath(drop_rate)
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Callable, Optional
  import torch.nn
  import torch
  from torch import Tensor
  from utils import *
label_desc: Use image node_id 1 to create a new FusedMBConv object from inherited base classes: nn with example: obj = FusedMBConv(kernel_size, input_c, out_c, expand_ratio, stride, se_ratio, drop_rate, norm_layer)