code: def same_model(model1, model2):
      for p1, p2 in zip(model1.parameters(), model2.parameters()):
          if p1.data.ne(p2.data).sum() > 0:
              return False
      return True
  
file_path: dgl/examples/pytorch/hilander/PSS/Smooth_AP/src/get_features.py
imports: import warnings
  import argparse
  import datetime
  import os
  import random
  import matplotlib
  import numpy
  import auxiliaries
  import datasets
  import evaluate
  import losses
  import netlib
  import torch.multiprocessing
label_desc: Use image node_id 1 for calling a global function with example usage: same_model(model1, model2) and returns: True, False