def METHOD_NAME(tmp_path):
    model = BigPruningTestModel()
    nncf_config = get_basic_pruning_config(input_sample_size=[1, 1, 8, 8])
    nncf_config["compression"]["pruning_init"] = 0.5
    nncf_config["compression"]["algorithm"] = "filter_pruning"
    onnx_model_proto = load_exported_onnx_version(nncf_config, model, path_to_storage_dir=tmp_path)
    # Check that conv2 + BN were pruned by output filters
    # WARNING: starting from at least torch 1.7.0, torch.onnx.export will fuses BN into previous
    # convs if torch.onnx.export is done with `training=False`, so this test might fail.
    check_bias_and_weight_shape("nncf_module.conv2", onnx_model_proto, [16, 16, 3, 3], [16])
    check_bias_and_weight_shape("nncf_module.bn", onnx_model_proto, [16], [16])
    # Check that up was pruned by input filters
    check_bias_and_weight_shape("nncf_module.up", onnx_model_proto, [16, 32, 3, 3], [32])
    # Check that conv3 was pruned by input filters
    check_bias_and_weight_shape("nncf_module.conv3", onnx_model_proto, [1, 32, 5, 5], [1])