code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      act_layer=nn.GELU,
      drop=0.0,
      extra_relu=False,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      self.fc1 = nn.Linear(in_features, hidden_features)
      self.relu = nn.ReLU() if extra_relu else nn.Identity()
      self.dwconv = nn.Conv2d(
          hidden_features,
          hidden_features,
          3,
          1,
          1,
          bias=True,
          groups=hidden_features,
      )
      self.act = act_layer()
      self.fc2 = nn.Linear(hidden_features, out_features)
      self.drop = nn.Dropout(drop)
  
imports: import math
  from typing import Tuple, List, Callable, Union
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, to_ntuple, trunc_normal_, LayerNorm, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new MlpWithDepthwiseConv object from inherited base classes: nn with example: obj = MlpWithDepthwiseConv(in_features, hidden_features, out_features, act_layer, drop, extra_relu)