class_docstr: Tests PyTorch and Tensorflow model imports
code: def _check(self, encrypted_tensor, reference, msg, tolerance=None):
      if tolerance is None:
          tolerance = getattr(self, "default_tolerance", 0.05)
      tensor = encrypted_tensor.get_plain_text()
  
      # Check sizes match
      self.assertTrue(tensor.size() == reference.size(), msg)
  
      if is_float_tensor(reference):
          diff = (tensor - reference).abs_()
          norm_diff = diff.div(tensor.abs() + reference.abs()).abs_()
          test_passed = norm_diff.le(tolerance) + diff.le(
              tolerance * 0.2
          )
          test_passed = test_passed.gt(0).all().item() == 1
      else:
          test_passed = (tensor == reference).all().item() == 1
      if not test_passed:
          logging.info(msg)
          logging.info("Result %s" % tensor)
          logging.info("Result - Reference = %s" % (tensor - reference))
      self.assertTrue(test_passed, msg=msg)
  
imports: import collections
  import io
  import logging
  import os
  import unittest
  import crypten
  import torch
  from crypten.common.tensor_types import is_float_tensor
  from crypten.config import cfg
  from crypten.nn import onnx_converter
  from test.multiprocess_test_case import get_random_test_tensor, MultiProcessTestCase, onehot
label_desc: Use image node_id 1 for calling the TestOnnxConverter obj's underlying member method code with example usage: obj._check(encrypted_tensor, reference, msg, tolerance) without return types