code: def __init__(
      self,
      in_features,
      activation,
      hidden_features=None,
      out_features=None,
      drop=0.0,
  ):
      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.dwconv = DWConv(hidden_features)
      self.act = instantiate_activation_block(activation)
      self.fc2 = nn.Linear(hidden_features, out_features)
      self.drop = nn.Dropout(drop)
  
      self.apply(self._init_weights)
  
imports: import math
  from functools import partial
  from typing import Optional
  import torch
  import torch.nn
  from pytorch_toolbelt.modules.interfaces import AbstractEncoder, FeatureMapsSpecification
  from pytorch_toolbelt.modules.activations import instantiate_activation_block, ACT_GELU
  from pytorch_toolbelt.modules.drop_path import DropPath
label_desc: Use image node_id 1 to create a new Mlp object from inherited base classes: nn with example: obj = Mlp(in_features, activation, hidden_features, out_features, drop)