code: def read_ppi_data(path):
      nxG = nx.MultiDiGraph()
  
      with open(path, newline="") as csvfile:
          reader = csv.reader(
              csvfile, delimiter=",", quoting=csv.QUOTE_NONNUMERIC
          )
          next(reader)
          for row in reader:
              # default field values for ppi edges (edge_type 0)
              if nxG.has_edge(row[0], row[1]):
                  raise RuntimeError("PPI has repeated edges")
              nxG.add_edge(
                  row[0],
                  row[1],
                  edge_type=0,
                  edge_de=0,
                  edge_dose=0,
                  edge_pert_time=0,
              )
              # self loop
              if row[0] == row[1]:
                  continue
              nxG.add_edge(
                  row[1],
                  row[0],
                  edge_type=0,
                  edge_de=0,
                  edge_dose=0,
                  edge_pert_time=0,
              )
      return nxG
  
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 7 for calling a global function with example usage: read_ppi_data(path) and returns: nxG