code: def check_pruned_simple_model(
      model: torch.nn.Module,
      ori_model: torch.nn.Module,
      dummy_input,
      model_type,
  ):
      model(dummy_input)
      if model_type == "lightning":
          model = model.model
          ori_model = ori_model.model
      assert (
          1
          - model.conv1.weight.shape[0]
          / ori_model.conv1.weight.shape[0]
          == 0.75
      )
      assert (
          1
          - model.conv2.weight.shape[0]
          / ori_model.conv2.weight.shape[0]
          == 0.75
      )
      assert (
          1
          - model.conv3.weight.shape[0]
          / ori_model.conv3.weight.shape[0]
          == 0.75
      )
      assert (
          1 - model.fc1.weight.shape[0] / ori_model.fc1.weight.shape[0]
          == 0.75
      )
  
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 2 for calling a global function with example usage: check_pruned_simple_model(model, ori_model, dummy_input, model_type) without return types