code: def load_model(args):
      if args["model"] == "MWE-GCN":
          model = MWE_GCN(
              n_input=args["in_feats"],
              n_hidden=args["hidden_feats"],
              n_output=args["out_feats"],
              n_layers=args["n_layers"],
              activation=torch.nn.Tanh(),
              dropout=args["dropout"],
              aggr_mode=args["aggr_mode"],
              device=args["device"],
          )
      elif args["model"] == "MWE-DGCN":
          model = MWE_DGCN(
              n_input=args["in_feats"],
              n_hidden=args["hidden_feats"],
              n_output=args["out_feats"],
              n_layers=args["n_layers"],
              activation=torch.nn.ReLU(),
              dropout=args["dropout"],
              aggr_mode=args["aggr_mode"],
              residual=args["residual"],
              device=args["device"],
          )
      else:
          raise ValueError("Unexpected model {}".format(args["model"]))
  
      return model
  
file_path: dgl/examples/pytorch/ogb/ogbn-proteins/utils.py
imports: import random
  import numpy
  import torch
  import torch.nn.functional
  from models import MWE_DGCN, MWE_GCN
label_desc: Use image node_id 2 for calling a global function with example usage: load_model(args) and returns: model