code: def __init__(
      self,
      channels,
      act="swish",
      norm=None,
      bias=True,
      drop=0.0,
      last_lin=False,
  ):
      m = []
  
      for i in range(1, len(channels)):
          m.append(Linear(channels[i - 1], channels[i], bias))
  
          if (i == len(channels) - 1) and last_lin:
              pass
          else:
              if norm is not None and norm.lower() != "none":
                  m.append(norm_layer(norm, channels[i]))
              if act is not None and act.lower() != "none":
                  m.append(act_layer(act))
              if drop > 0:
                  m.append(nn.Dropout(drop))
  
      self.m = m
      super(MLP, self).__init__(*self.m)
  
imports: import numpy
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddle.distributed
  import pgl
  import pgl.nn
  from pgl.nn import functional
  from pgl.utils.logger import log
  import models.mol_encoder
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: paddle.nn with example: obj = MLP(channels, act, norm, bias, drop, last_lin)