code: def __init__(
      self,
      in_features: int,
      hidden_features: Optional[int] = None,
      out_features: Optional[int] = None,
      act_layer: Callable[..., nn.Module] = nn.GELU,
      drop: float = 0.0,
      bias: bool = True,
  ) -> None:
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
      self.act = act_layer()
      self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
      self.drop = nn.Dropout(drop)
  
imports: import logging
  import math
  from functools import partial
  from itertools import repeat
  from typing import Callable, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.runner import BaseModule, load_checkpoint
  from mmseg.ops import resize
  from mmseg.utils import get_root_logger
  from torch import Tensor
  from .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, hidden_features, out_features, act_layer, drop, bias)