def METHOD_NAME():
    model = SimpleTorchModel()
    pruning_config_list = [{
        'op_names': ['conv1', 'fc1'],
        'target_names': ['weight', 'bias'],
        'sparse_ratio': 0.4
    }]
    wrappers, _ = register_wrappers(model, pruning_config_list, mode='pruning')
    assert set(wrappers.keys()) == set(['conv1', 'fc1'])
    quantization_config_list = [{
        'op_names': ['conv2', 'fc1'],
        'target_names': ['_input_', 'weight', '_output_'],
        'quant_dtype': 'int8',
        'quant_scheme': 'affine'
    }]
    wrappers, _ = register_wrappers(model, quantization_config_list, mode='quantization', existed_wrappers=wrappers)
    assert set(wrappers.keys()) == set(['conv1', 'conv2', 'fc1'])
    distillation_config_list = [{
        'op_names': ['conv3', 'fc1'],
        'target_names': ['_output_'],
        'lambda': 0.1
    }]
    wrappers, _ = register_wrappers(model, distillation_config_list, mode='distillation', existed_wrappers=wrappers)
    assert set(wrappers.keys()) == set(['conv1', 'conv2', 'conv3', 'fc1'])