code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      act_layer=nn.GELU,
      drop=0.0,
      mid_conv=False,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      self.mid_conv = mid_conv
      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)
  
      if self.mid_conv:
          self.mid = nn.Conv2D(
              hidden_features,
              hidden_features,
              kernel_size=3,
              stride=1,
              padding=1,
              groups=hidden_features,
          )
  
      self.norm1 = nn.BatchNorm2D(hidden_features)
      self.norm2 = nn.BatchNorm2D(out_features)
      self.init_weight()
  
imports: import math
  import itertools
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models.backbones.transformer_utils import *
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, mid_conv)