code: def message(self, x_i, x_j, edge_feature):
      # x_i has shape [E, in_channels]
      # x_j has shape [E, in_channels]
      tmp = torch.cat(
          [x_i, x_j, edge_feature], dim=1
      )  # tmp has shape [E, 2 * in_channels]
      return self.message_mlp(tmp)
  
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.graph import Graph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 3 for calling the MlpMessageConv obj's underlying member method code with example usage: obj.message(x_i, x_j, edge_feature) and returns: self