code: def __init__(
      self,
      in_chs,
      out_chs,
      stride=1,
      dilation=1,
      pad_type="",
      no_skip=False,
      act_layer=nn.ReLU,
      norm_layer=None,
  ):
      super(PreXceptionModule, self).__init__()
      out_chs = to_3tuple(out_chs)
      self.in_channels = in_chs
      self.out_channels = out_chs[-1]
      self.no_skip = no_skip
      if not no_skip and (
          self.out_channels != self.in_channels or stride != 1
      ):
          self.shortcut = create_conv2d(
              in_chs, self.out_channels, 1, stride=stride
          )
      else:
          self.shortcut = nn.Identity()
  
      self.norm = get_norm_act_layer(norm_layer, act_layer=act_layer)(
          in_chs, inplace=True
      )
      self.stack = nn.Sequential()
      for i in range(3):
          self.stack.add_module(
              f"conv{i + 1}",
              PreSeparableConv2d(
                  in_chs,
                  out_chs[i],
                  3,
                  stride=stride if i == 2 else 1,
                  dilation=dilation,
                  padding=pad_type,
                  act_layer=act_layer,
                  norm_layer=norm_layer,
                  first_act=i > 0,
              ),
          )
          in_chs = out_chs[i]
  
imports: from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import ClassifierHead, ConvNormAct, create_conv2d, get_norm_act_layer
  from timm.layers.helpers import to_3tuple
  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 PreXceptionModule object from inherited base classes: nn with example: obj = PreXceptionModule(in_chs, out_chs, stride, dilation, pad_type, no_skip, act_layer, norm_layer)