code: def test(self, min_lr, step_rate, step_size, staircase):
      config_dict = {
          "train_batch_size": 2,
          "steps_per_print": 1,
          "optimizer": {
              "type": "Adam",
              "params": {"lr": 0.00015},
          },
          "scheduler": {
              "type": LR_RANGE_TEST,
              "params": {
                  LR_RANGE_TEST_MIN_LR: min_lr,
                  LR_RANGE_TEST_STEP_RATE: step_rate,
                  LR_RANGE_TEST_STEP_SIZE: step_size,
                  LR_RANGE_TEST_STAIRCASE: staircase,
              },
          },
          "gradient_clipping": 1.0,
      }
      hidden_dim = 10
  
      model = SimpleModel(hidden_dim, empty_grad=False)
      model, _, _, lr_scheduler = deepspeed.initialize(
          config=config_dict,
          model=model,
          model_parameters=model.parameters(),
      )
      data_loader = random_dataloader(
          model=model,
          total_samples=max(50, step_size * 2),
          hidden_dim=hidden_dim,
          device=model.device,
          dtype=torch.float,
      )
  
      step_lrs = []
      for _, batch in enumerate(data_loader):
          step_lrs.extend(lr_scheduler.get_lr())
          loss = model(batch[0], batch[1])
          model.backward(loss)
          model.step()
  
      # Verify starting lr
      assert step_lrs[0] == min_lr
  
      if staircase:
          # Verify staircase increasing lr
          _verify_staircase_increase(step_lrs, step_size)
      else:
          # Verify continuous increasing lr
          _verify_continuous_increase(step_lrs)
  
imports: import torch
  import deepspeed
  import pytest
  from unit.common import DistributedTest
  from unit.simple_model import SimpleModel, random_dataloader
  from deepspeed.runtime.lr_schedules import LR_RANGE_TEST, LR_RANGE_TEST_MIN_LR, LR_RANGE_TEST_STEP_RATE, LR_RANGE_TEST_STEP_SIZE, LR_RANGE_TEST_STAIRCASE
  from deepspeed.runtime.lr_schedules import WARMUP_LR, WARMUP_MIN_LR, WARMUP_MAX_LR, WARMUP_NUM_STEPS, WARMUP_TYPE, WARMUP_LOG_RATE, WARMUP_LINEAR_RATE
  from deepspeed.runtime.lr_schedules import ONE_CYCLE, CYCLE_MIN_LR, CYCLE_MAX_LR, CYCLE_FIRST_STEP_SIZE, DECAY_LR_RATE, DECAY_STEP_SIZE
  from deepspeed.runtime.lr_schedules import CYCLE_MIN_MOM, CYCLE_MAX_MOM, DECAY_MOM_RATE
  from deepspeed.runtime.lr_schedules import WARMUP_DECAY_LR, TOTAL_NUM_STEPS
  from deepspeed.runtime.lr_schedules import WARMUP_COSINE_LR, WARMUP_MIN_RATIO, COS_MIN_RATIO
label_desc: Use image node_id 1 for calling the TestLrRange obj's underlying member method code with example usage: obj.test(min_lr, step_rate, step_size, staircase) without return types