pzmm.MLFlowModel.read_mlflow_model_file() failed with JSONDecodeError: Extra data

Describe the issue
Trying to read mlflow model using pzmm.MLFlowModel.read_mlflow_model_file result in JSONDecodeError. I'm just using a simple example from here:
https://medium.com/@rehabreda/registering-mlflow-models-to-sas-model-manager-using-sasctl-a-comprehensive-guide-a47dbf183338

To Reproduce
The rest of the training code can be found on the above link. The code that perform the read mlflow model file is shown below:

## define randomforest model 
model = RandomForestClassifier(n_estimators=300).fit(x_train, y_train)

##Model signature defines schema of model input and output
signature = infer_signature(x_train, model.predict(x_train))

## log model score to mlflow
score = model.score(x_test, y_test)
print("Score: %s" % score)
mlflow.log_metric("score", score)

### log model 
mlflow.sklearn.log_model(model, "model", signature=signature)
print("Model saved in run %s" % mlflow.active_run().info.run_uuid)

mlPath = Path(f'./mlruns/1/{mlflow.active_run().info.run_uuid}/artifacts/model')

## get info aboud model variables ,input and output
varDict, inputsDict, outputsDict = pzmm.MLFlowModel.read_mlflow_model_file(mlPath)

Expected behavior
Getting the dictionary successfully from pzmm.MLFlowModel.read_mlflow_model_file().

Stack Trace
If you're experiencing an exception, include the full stack trace and error message.

---------------------------------------------------------------------------
JSONDecodeError                           Traceback (most recent call last)
Cell In[4], line 4
      1 mlPath = Path(f'./mlruns/1/{mlflow.active_run().info.run_uuid}/artifacts/model')
      3 ## get info aboud model variables ,input and output
----> 4 varDict, inputsDict, outputsDict = pzmm.MLFlowModel.read_mlflow_model_file(mlPath)

File ~\AppData\Local\miniconda3\envs\ml\Lib\site-packages\sasctl\pzmm\mlflow_model.py:56, in MLFlowModel.read_mlflow_model_file(cls, m_path)
     53     outputs = m_lines[ind_out[0] : -1]
     55     inputs_dict = json.loads("".join([s.strip() for s in inputs])[9:-1])
---> 56     outputs_dict = json.loads("".join([s.strip() for s in outputs])[10:-1])
     57 else:
     58     raise ValueError(
     59         "Improper or unset signature values for model. No input or output "
     60         "dicts could be generated. "
     61     )

File ~\AppData\Local\miniconda3\envs\ml\Lib\json\__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
    341     s = s.decode(detect_encoding(s), 'surrogatepass')
    343 if (cls is None and object_hook is None and
    344         parse_int is None and parse_float is None and
    345         parse_constant is None and object_pairs_hook is None and not kw):
--> 346     return _default_decoder.decode(s)
    347 if cls is None:
    348     cls = JSONDecoder

File ~\AppData\Local\miniconda3\envs\ml\Lib\json\decoder.py:340, in JSONDecoder.decode(self, s, _w)
    338 end = _w(s, end).end()
    339 if end != len(s):
--> 340     raise JSONDecodeError("Extra data", s, end)
    341 return obj

JSONDecodeError: Extra data: line 1 column 73 (char 72)

Version
1.10.0

Inizia in sasctl/pzmm/mlflow_model.py, in MLFlowModel.read_mlflow_model_file(), soprattutto nell’analisi di inputs_dict e outputs_dict mostrata nello stack trace. Riproduci l’esempio con la directory del modello MLflow indicata e ispeziona i metadati del modello generati; il lavoro è completato quando il metodo restituisce i tre dizionari attesi senza JSONDecodeError.