code: def test_norm_pruner(model_type: str, prune_type: str):
      model, config_list_dict, dummy_input = create_model(model_type)
  
      config_list = config_list_dict["pruning"]
      config_list = auto_set_denpendency_group_ids(
          model, config_list, dummy_input
      )
      pruner = pruner_dict[prune_type](model, config_list)
      pruner.compress()
      pruner.unwrap_model()
  
      masks = pruner.get_masks()
      check_masks(masks, model_type)
  
      pruned_model = ModelSpeedup(
          model, dummy_input, masks
      ).speedup_model()
      ori_model, _, _ = create_model(model_type)
      check_pruned_simple_model(
          pruned_model, ori_model, dummy_input, model_type
      )
  
file_path: nni/test/algo/compression/pruning/test_pruner.py
imports: import pytest
  from typing import Dict, Type
  import torch
  from nni.compression.pruning import LevelPruner, L1NormPruner, L2NormPruner, FPGMPruner, SlimPruner, TaylorPruner, MovementPruner, LinearPruner, AGPPruner
  from nni.compression.base.compressor import Pruner
  from nni.compression.utils import auto_set_denpendency_group_ids
  from nni.compression.speedup import ModelSpeedup
  from ..assets.common import create_model
  from ..assets.simple_mnist import create_lighting_evaluator, create_pytorch_evaluator
label_desc: Use image node_id 3 for calling a global function with example usage: test_norm_pruner(model_type, prune_type) without return types