code: def __init__(self, *args, **kwargs):
      super(TestFusedNovoGrad, self).__init__(*args, **kwargs)
  
      # The options for NovoGrad and FusedNovoGrad are very specific if they
      # are expected to behave the same.
      self.options = {
          "lr": 1e-3,
          "betas": (0.95, 0),
          "eps": 1e-8,
          "weight_decay": 0,
          "grad_averaging": False,
          "amsgrad": False,
      }
  
      self.tst_options = {
          "lr": 1e-3,
          "betas": (0.95, 0),
          "eps": 1e-8,
          "weight_decay": 0,
          "grad_averaging": False,
          "amsgrad": False,
          "bias_correction": False,
          "reg_inside_moment": True,
          "norm_type": 2,
          "init_zero": False,
          "set_grad_none": True,
      }
  
      self.ref_optim = Novograd
      self.fused_optim = apex.optimizers.FusedNovoGrad
  
file_path: apex/tests/L0/run_optimizers/test_fused_novograd.py
imports: import torch
  from torch.optim import Optimizer
  import math
  import apex
  import unittest
  from test_fused_optimizer import TestFusedOptimizer
  from itertools import product
label_desc: Use image node_id 1 to create a new TestFusedNovoGrad object from inherited base classes: TestFusedOptimizer with example: obj = TestFusedNovoGrad()