code: def test_some_overflow(self):
      config_dict = {
          "train_batch_size": 1,
          "steps_per_print": 1,
          "optimizer": {"type": "Lamb", "params": {"lr": 0.00015}},
          "fp16": {
              "enabled": True,
              "loss_scale": 0,
              "initial_scale_power": 8,
              "loss_scale_window": 2,
          },
      }
      hidden_dim = 1
      model = SimpleModel(hidden_dim)
      model, optim, _, _ = deepspeed.initialize(
          config=config_dict,
          model=model,
          model_parameters=model.parameters(),
      )
  
      expected_loss_scale = 2**8
      expected_scale_window = 2
      expected_iteration = 0
      # Ensure the dynamic loss scaler is correctly configured.
      assert optim.dynamic_loss_scale == True
      assert optim.cur_scale == expected_loss_scale
      assert optim.scale_window == expected_scale_window
  
      # Run model with overflows to decrease scale
      overflow_gradients = [float("inf"), float("nan")]
      expected_iteration += len(overflow_gradients)
      run_model_step(model, overflow_gradients)
      expected_loss_scale /= 2 ** len(overflow_gradients)
      assert optim.cur_scale == expected_loss_scale
      assert optim.cur_iter == expected_iteration
  
      # Run model scale_window + 1 times to increase scale once
      normal_gradients = np.random.uniform(
          -0.1, 0.1, expected_scale_window + 1
      )
      expected_iteration += len(normal_gradients)
      run_model_step(model, normal_gradients)
      expected_loss_scale *= 2
      assert optim.cur_scale == expected_loss_scale
      assert optim.cur_iter == expected_iteration
  
      # Run model with overflows to decrease scale
      overflow_gradients = [float("inf")]
      expected_iteration += len(overflow_gradients)
      run_model_step(model, overflow_gradients)
      expected_loss_scale /= 2 ** len(overflow_gradients)
      assert optim.cur_scale == expected_loss_scale
      assert optim.cur_iter == expected_iteration
  
file_path: DeepSpeed/tests/unit/runtime/half_precision/test_dynamic_loss_scale.py
imports: import torch
  import deepspeed
  import numpy
  from unit.common import DistributedTest
  from unit.simple_model import SimpleModel
label_desc: Use image node_id 3 for calling the TestUnfused obj's underlying member method code with example usage: obj.test_some_overflow() without return types