code: def gen_single_type_test(
      self,
      param_type=torch.float,
      device="cuda",
      *,
      skip_assert: bool = False,
  ):
      nelem = 278011
  
      # Some ref and test optimizers may require different set of options.
      # This is a quick workaround to add that functionality while making
      # minimum changes in existing code.
      # If there is no "tst_options" field provided, safe to initialize
      # the test optimizer with the parameters of reference optimizer.
      if not hasattr(self, "tst_options"):
          self.tst_options = self.options
  
      tensor = torch.rand(nelem, dtype=param_type, device=device)
  
      ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim(
          [tensor], self.options, self.tst_options
      )
  
      for i in range(self.iters):
          self.gen_grad(ref_param, tst_param)
          ref_optim.step()
          tst_optim.step()
          if skip_assert:
              return
          max_abs_diff, max_rel_diff = self.get_max_diff(
              ref_param, tst_param
          )
          self.assertLessEqual(max_abs_diff, self.max_abs_diff)
          self.assertLessEqual(max_rel_diff, self.max_rel_diff)
  
imports: from itertools import product
  import random
  import unittest
  import torch
  import apex
label_desc: Use image node_id 7 for calling the TestFusedOptimizer obj's underlying member method code with example usage: obj.gen_single_type_test(param_type, device) without return types