code: def unwrap_schedule(scheduler, num_steps=10):
      lrs = []
      for _ in range(num_steps):
          lrs.append(scheduler.get_lr()[0])
          scheduler.step()
      return lrs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/optimization/test_optimization.py
imports: import os
  import tempfile
  import unittest
  from transformers import is_torch_available
  from transformers.testing_utils import require_torch
label_desc: Use image node_id 1 for calling a global function with example usage: unwrap_schedule(scheduler, num_steps) and returns: lrs