code: def test_polynomial_scheduler_range_str(range_str):
      pruner_config = MagnitudePrunerConfig.from_dict(
          {"global_config": {"scheduler": {"update_steps": range_str}}}
      )
  
      update_steps_tensor = torch.tensor(list(eval(range_str)))
      assert torch.all(
          pruner_config.global_config.scheduler.update_steps
          == update_steps_tensor
      )
  
imports: import copy
  from collections import OrderedDict
  import numpy
  import pytest
  import torch
  from coremltools.optimize.torch.pruning import MagnitudePruner, MagnitudePrunerConfig, ModuleMagnitudePrunerConfig
  from coremltools.optimize.torch.pruning._utils import n_m_mask
label_desc: Use image node_id 22 for calling a global function with example usage: test_polynomial_scheduler_range_str(range_str) without return types