code: def gen_single_type_test(self, param_type=torch.float, device="cuda"):
      nelem = 18011
      tensor = torch.rand(nelem, dtype=param_type, device=device)
      weight_decay = [0, 0.01]
  
      for wd in weight_decay:
          lamb_option = {
              "lr": 5e-4,
              "betas": (0.9, 0.999),
              "eps": 1e-08,
              "weight_decay": wd,
          }
          (
              ref_param,
              tst_param,
              ref_optim,
              tst_optim,
          ) = self.gen_param_optim([tensor], lamb_option)
  
          if isinstance(
              tst_optim, apex.optimizers.FusedMixedPrecisionLamb
          ):
              if param_type != torch.float:
                  # joseli: This parameter is usually passed into the constructor,
                  # but I do not want to change the testing interface.
                  # As long as this parameter is set before the first call to step(),
                  # then it should act normally.
                  tst_optim.reduced_precision_dtype = param_type
          for i in range(self.iters):
              self.gen_grad(ref_param, tst_param)
              ref_optim.step()
              torch.cuda.synchronize()
              tst_optim.step()
              torch.cuda.synchronize()
              torch.testing.assert_close(tst_param, ref_param)
  
imports: import unittest
  import os
  import torch
  from torch.optim import Optimizer
  import apex
  from apex.multi_tensor_apply import multi_tensor_applier
  from itertools import product
label_desc: Use image node_id 7 for calling the TestLamb obj's underlying member method code with example usage: obj.gen_single_type_test(param_type, device) without return types