code: def build_conv_model(self, model_type):
      if model_type == "GCN":
          return pyg_nn.GCNConv
      elif model_type == "GAT":
          return pyg_nn.GATConv
      elif model_type == "GraphSage":
          return pyg_nn.SAGEConv
      else:
          raise ValueError(
              "Model {} unavailable, please add it to GNN.build_conv_model.".format(
                  model_type
              )
          )
  
imports: import torch
  import argparse
  import numpy
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  import torch_geometric.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from sklearn.metrics import *
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.build_conv_model(model_type) and returns: pyg_nn, pyg_nn, pyg_nn