code: def forward(self, x, edge_index, edge_feature):
      # x has shape [N, in_channels]
      # edge_index has shape [2, E]
      if self.pre_conv is not None:
          x = self.pre_conv(x)
      return self.propagate(edge_index, x=x, edge_feature=edge_feature)
  
imports: import argparse
  import copy
  import csv
  import numpy
  import time
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.tensorboard import SummaryWriter
  import networkx
  import numpy
  import sklearn.metrics
  import torch_geometric.transforms
  import torch_geometric.nn
  from torch.utils.data import DataLoader
  from deepsnap.hetero_graph import HeteroGraph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 2 for calling the MlpMessageConv obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_feature) and returns: self