run_onnx_squad.py fails with "Model requires 4 inputs. Input Feed contains 3"

run_onnx_squad.py from
https://github.com/onnx/models/tree/master/text/machine_comprehension/bert-squad
fails with exception:
"Model requires 4 inputs. Input Feed contains 3"

Steps to repro:
clone that repo

git lfs fetch --include=text/machine_comprehension/bert-squad/model/bertsquad10.onnx.tar.gz

wait for download

extract the real onnx file from the local git cache (could nt find a better way):
$ file ~/repos/models/.git/lfs/objects/1c/ec/1cec14b36fac3e09b2ea54b8de297e12bafabd0fb9d123ad10b6d45459a835a6
/home/wtambellini/repos/models/.git/lfs/objects/1c/ec/1cec14b36fac3e09b2ea54b8de297e12bafabd0fb9d123ad10b6d45459a835a6: gzip compressed data, was "bert.onnx.onnx",
to
text/machine_comprehension/bert-squad/model/bertsquad10.onnx

create the inputs.json as explained there:
https://github.com/onnx/models/tree/master/text/machine_comprehension/bert-squad

download the vocab file from the zip bert model from the bert repo :
https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip

install a recent onnxrt :
$ sudo pip3.5 install --upgrade onnxruntime
Collecting onnxruntime
Downloading https://files.pythonhosted.org/packages/2a/26/52b66fcea1a79b1c873df22bc9844895e6b1ef356c5bb7ee4da260af2ad2/onnxruntime-0.5.0-cp35-cp35m-manylinux2010_x86_64.whl (3.2MB)
100% |████████████████████████████████| 3.2MB 219kB/s
Installing collected packages: onnxruntime
Found existing installation: onnxruntime 0.2.1
Uninstalling onnxruntime-0.2.1:
Successfully uninstalled onnxruntime-0.2.1
Successfully installed onnxruntime-0.5.0

try to run a simple inference :
python3.5 dependencies/run_onnx_squad.py --model model/bertsquad10.onnx --vocab_file ~/Downloads/bert/uncased_L-12_H-768_A-12/vocab.txt --predict_file inputs.json --output /tmp

See that the onnx expects 4 inputs but the py script only gives 3 :

onnxrt expected inputs:
NodeArg(name='unique_ids_raw_output___9:0', type='tensor(int64)', shape=['unk__485'])
NodeArg(name='segment_ids:0', type='tensor(int64)', shape=['unk__486', 256])
NodeArg(name='input_mask:0', type='tensor(int64)', shape=['unk__487', 256])
NodeArg(name='input_ids:0', type='tensor(int64)', shape=['unk__488', 256])

input data is created line 556 with :
data = {"input_ids:0": input_ids[idx:idx + bs],
"input_mask:0": input_mask[idx:idx + bs],
"segment_ids:0": segment_ids[idx:idx + bs]
}
so indeed, 'unique_ids_raw_output___9:0' is missing.

According to the doc, the missing input is :
"label_ids: one-hot encoded labels for the text "

Kind