code: def __init__(
      self,
      nfeat,
      nhid,
      nclass,
      input_droprate,
      hidden_droprate,
      use_bn=False,
  ):
      super(MLP, self).__init__()
  
      self.layer1 = nn.Linear(nfeat, nhid, bias=True)
      self.layer2 = nn.Linear(nhid, nclass, bias=True)
  
      self.input_dropout = nn.Dropout(input_droprate)
      self.hidden_dropout = nn.Dropout(hidden_droprate)
      self.bn1 = nn.BatchNorm1d(nfeat)
      self.bn2 = nn.BatchNorm1d(nhid)
      self.use_bn = use_bn
  
file_path: dgl/examples/pytorch/grand/model.py
imports: import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: nn with example: obj = MLP(nfeat, nhid, nclass, input_droprate, hidden_droprate, use_bn)