code: def __init__(
      self,
      dw_ksize,
      expand_ksize,
      project_ksize,
      in_channels,
      out_channels,
      expand_ratio,
      id_skip,
      strides,
      se_ratio,
      swish,
      dilated,
  ):
      super().__init__()
  
      self.id_skip = (
          id_skip
          and all(s == 1 for s in strides)
          and in_channels == out_channels
      )
  
      act_fn = lambda: Swish() if swish else nn.ReLU(True)
  
      layers = []
      expaned_ch = in_channels * expand_ratio
      if expand_ratio != 1:
          expand = nn.Sequential(
              nn.Conv2d(
                  in_channels, expaned_ch, expand_ksize, bias=False
              ),
              nn.BatchNorm2d(expaned_ch),
              act_fn(),
          )
          layers.append(expand)
  
      depthwise = nn.Sequential(
          MDConv(expaned_ch, dw_ksize, strides, bias=False),
          nn.BatchNorm2d(expaned_ch),
          act_fn(),
      )
      layers.append(depthwise)
  
      if se_ratio > 0:
          se = SEModule(expaned_ch, int(expaned_ch * se_ratio))
          layers.append(se)
  
      project = nn.Sequential(
          nn.Conv2d(
              expaned_ch, out_channels, project_ksize, bias=False
          ),
          nn.BatchNorm2d(out_channels),
      )
      layers.append(project)
  
      self.layers = nn.Sequential(*layers)
  
imports: import torch.nn
  from .layers import Flatten
  from .layers import SEModule
  from .layers import Swish
  from .mdconv import MDConv
  from .utils import MixnetDecoder
  from .utils import round_filters
label_desc: Use image node_id 1 to create a new MixBlock object from inherited base classes: nn with example: obj = MixBlock(dw_ksize, expand_ksize, project_ksize, in_channels, out_channels, expand_ratio, id_skip, strides, se_ratio, swish, dilated)