code: def parameters_are_equal(model1, model2):
      output = True
      for p1, p2 in zip(model1.parameters(), model2.parameters()):
          output &= torch.allclose(p1.data, p2.data, rtol=1e-2)
      return output
  
imports: import logging
  import os
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.optim
  from torch.nn.parallel import DistributedDataParallel
  from pytorch_metric_learning.losses import ContrastiveLoss, CrossBatchMemory
  from pytorch_metric_learning.miners import PairMarginMiner
  from pytorch_metric_learning.utils import distributed
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 1 for calling a global function with example usage: parameters_are_equal(model1, model2) and returns: output