def METHOD_NAME(tmp_path, prune_first, ref_shapes):
    model = PruningTestModelConcat()
    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"] = prune_first
    nncf_config["compression"]["pruning_init"] = 0.5
    onnx_model_proto = load_exported_onnx_version(nncf_config, model, path_to_storage_dir=tmp_path)
    for i in range(1, 5):
        conv_name = "nncf_module.conv{}".format(i)
        check_bias_and_weight_shape(conv_name, onnx_model_proto, *ref_shapes[i - 1])