code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      act_layer=nn.ReLU,
      drop=0.0,
      lr_mult=1.0,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      self.fc1 = Conv2DBN(in_features, hidden_features, lr_mult=lr_mult)
      param_attr = paddle.ParamAttr(learning_rate=lr_mult)
      self.dwconv = nn.Conv2D(
          hidden_features,
          hidden_features,
          3,
          1,
          1,
          groups=hidden_features,
          weight_attr=param_attr,
          bias_attr=param_attr,
      )
      self.act = act_layer()
      self.fc2 = Conv2DBN(
          hidden_features, out_features, lr_mult=lr_mult
      )
      self.drop = nn.Dropout(drop)
  
imports: from functools import partial
  from collections import defaultdict
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddleseg
  from paddleseg import utils
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.models.backbones.transformer_utils import Identity, DropPath
  from ppmatting.models.layers import MLFF
  from ppmatting.models.losses import MRSD, GradientLoss
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, lr_mult)