class_docstr: Tests PyTorch and Tensorflow model imports
code: def _compute_reference_parameters(
      self, init_name, reference, model, learning_rate
  ):
      for name, param in model.named_parameters(recurse=False):
          local_name = init_name + "_" + name
          reference[local_name] = (
              param.get_plain_text()
              - learning_rate * param.grad.get_plain_text()
          )
      for name, module in model._modules.items():
          local_name = init_name + "_" + name
          reference = self._compute_reference_parameters(
              local_name, reference, module, learning_rate
          )
      return reference
  
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 3 for calling the TestOnnxConverter obj's underlying member method code with example usage: obj._compute_reference_parameters(init_name, reference, model, learning_rate) and returns: reference