code: def test_compressor():
      # NOTE: not enough, need quantizer-update-calibration-config, distiller
      model = SimpleTorchModel()
      pruning_config_list = [
          {
              "op_names": ["conv1", "fc1"],
              "target_names": ["weight", "bias"],
              "sparse_ratio": 0.4,
          }
      ]
      pruner = Pruner(model, pruning_config_list)
  
      masks = defaultdict(dict)
      masks["conv1"]["weight"] = torch.ones_like(
          model.conv1.weight
      ).detach()
      masks["conv1"]["bias"] = torch.ones_like(
          model.conv1.bias
      ).detach()
      masks["fc1"]["weight"] = torch.ones_like(
          model.fc1.weight
      ).detach()
      masks["fc1"]["bias"] = torch.ones_like(model.fc1.bias).detach()
      pruner.update_masks(masks)
      masks = pruner.get_masks()
  
      quantization_config_list = [
          {
              "op_names": ["conv2", "fc1"],
              "target_names": ["_input_", "weight", "_output_"],
              "quant_dtype": "int8",
              "quant_scheme": "affine",
          }
      ]
      quantizer = Quantizer.from_compressor(
          pruner, quantization_config_list
      )
  
imports: from __future__ import annotations
  import pytest
  from collections import defaultdict
  import torch
  from nni.compression.base.compressor import Pruner, Quantizer
  from ..assets.simple_mnist import SimpleTorchModel
label_desc: Use image node_id 1 for calling a global function with example usage: test_compressor() without return types