code: def forward(self, x):
      for linear, bn in zip(self.linears[:-1], self.bns):
          x = linear(x)
          x = F.relu(x, inplace=True)
          x = bn(x)
          x = F.dropout(x, p=self.dropout, training=self.training)
      x = self.linears[-1](x)
      return F.log_softmax(x, dim=-1)
  
imports: import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the MLP obj's underlying member method code with example usage: obj.forward(x) and returns: F