code: def setUp(self):
      self.x = torch.tensor([2.0]).cuda().half()
      weight = 3.0
      bias = 5.0
      self.error = 1.0
      self.target = (
          torch.tensor([self.x * weight + bias + self.error])
          .cuda()
          .half()
      )
      self.loss_fn = torch.nn.L1Loss()
  
      self.model = torch.nn.Linear(1, 1)
      self.model.weight.data = torch.tensor([[weight]])
      self.model.bias.data = torch.tensor([bias])
      self.model.cuda().half()
      self.params = list(self.model.parameters())
  
      self.cfg_dls = OmegaConf.create(
          {
              "optimization": {
                  "lr": [0.1],
              },
              "optimizer": {
                  "_name": "adam",
                  "lr": [0.1],
                  "adam_betas": "(0.9, 0.999)",
                  "adam_eps": 1e-8,
                  "weight_decay": 0.0,
              },
              "common": {
                  "fp16_init_scale": 1,
                  "fp16_scale_window": 1,
                  "fp16_scale_tolerance": 1,
                  "threshold_loss_scale": 1,
                  "min_loss_scale": 1e-4,
                  "tpu": False,
              },
          }
      )
      logging.disable(logging.CRITICAL)
  
file_path: fairseq/tests/test_fp16_optimizer.py
imports: import copy
  import logging
  import unittest
  import torch
  from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer
  from omegaconf import OmegaConf
label_desc: Use image node_id 1 for calling the TestGradientScaling obj's underlying member method code with example usage: obj.setUp() without return types