code: def forward(self, emb_motif, emb_motif_mod):
      pred = self.mlp(torch.cat((emb_motif, emb_motif_mod), dim=1))
      pred = F.log_softmax(pred, dim=1)
      return pred
  
imports: from functools import reduce
  import random
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch_geometric.nn
  import torch_geometric.utils
  import utils
label_desc: Use image node_id 2 for calling the BaselineMLP obj's underlying member method code with example usage: obj.forward(emb_motif, emb_motif_mod) and returns: pred