code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      act_layer=nn.GELU,
      drop=0.0,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
      self.act = act_layer()
      self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
      self.drop = nn.Dropout(drop)
  
imports: from collections import OrderedDict
  import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  import numpy
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  from annotator.uniformer.mmcv_custom import load_checkpoint
  from annotator.uniformer.mmseg.utils import get_root_logger
  from ..builder import BACKBONES
label_desc: Use image node_id 1 to create a new CMlp object from inherited base classes: nn with example: obj = CMlp(in_features, hidden_features, out_features, act_layer, drop)