code: def get_model(name, params, metadata=None):
      Model = models_dict.get(name, None)
      assert Model is not None, f"Model {name} not supported!"
  
      if name == "rgat":
          return Model(
              metadata,
              params["hidden_channels"],
              params["num_layers"],
              params["output_channels"],
              params["num_heads"],
          )
  
      if name == "rgcn":
          return Model(
              metadata,
              params["hidden_channels"],
              params["num_layers"],
              params["output_channels"],
          )
  
      if name == "gat":
          return Model(
              params["inputs_channels"],
              params["hidden_channels"],
              params["num_layers"],
              params["output_channels"],
              heads=params["num_heads"],
          )
  
      if name == "pna":
          return Model(
              params["inputs_channels"],
              params["hidden_channels"],
              params["num_layers"],
              params["output_channels"],
              aggregators=["mean", "min", "max", "std"],
              scalers=["identity", "amplification", "attenuation"],
              deg=params["degree"],
          )
  
      return Model(
          params["inputs_channels"],
          params["hidden_channels"],
          params["num_layers"],
          params["output_channels"],
      )
  
file_path: pytorch_geometric/benchmark/utils/utils.py
imports: import os
  import os.path
  from datetime import datetime
  import torch
  from ogb.nodeproppred import PygNodePropPredDataset
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.data import HeteroData
  from torch_geometric.datasets import OGB_MAG, Reddit
  from torch_geometric.nn import GAT, GCN, PNA, EdgeCNN, GraphSAGE
  from torch_geometric.utils import index_to_mask
  from .hetero_gat import HeteroGAT
  from .hetero_sage import HeteroGraphSAGE
label_desc: Use image node_id 3 for calling a global function with example usage: get_model(name, params, metadata) and returns: Model, Model, Model, Model, Model