def METHOD_NAME(tmp_path):
    model = GroupNormModel()
    nncf_config = get_basic_pruning_config(input_sample_size=[1, 1, 8, 8])
    nncf_config["compression"]["algorithm"] = "filter_pruning"
    nncf_config["compression"]["params"]["prune_first_conv"] = True
    nncf_config["compression"]["pruning_init"] = 0.5
    onnx_model_proto = load_exported_onnx_version(nncf_config, model, path_to_storage_dir=tmp_path)
    check_bias_and_weight_shape("nncf_module.conv1", onnx_model_proto, [8, 1, 1, 1], [8])
    check_bias_and_weight_shape("nncf_module.conv2", onnx_model_proto, [16, 8, 1, 1], [16])