code: def test_mvp_pruner(model_type: str):
      model, config_list_dict, dummy_input = create_model(model_type)
      if model_type == "lightning":
          evaluator = create_lighting_evaluator()
      elif model_type == "pytorch":
          evaluator = create_pytorch_evaluator(model)
  
      config_list = config_list_dict["pruning"]
      config_list = auto_set_denpendency_group_ids(
          model, config_list, dummy_input
      )
      pruner = MovementPruner(
          model,
          config_list,
          evaluator=evaluator,
          warmup_step=10,
          cooldown_begin_step=90,
      )
      pruner.compress(100, None)
      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 5 for calling a global function with example usage: test_mvp_pruner(model_type) without return types