Using the yolov3 get_prediction_evaluation_yolov3_variant throws an exception.

Finding optimal threshold for each tensor using entropy algorithm ...
Number of tensors : 397
Number of histogram bins : 128 (The number may increase depends on the data it collects)
Number of quantized bins : 128
Traceback (most recent call last):
File "e2e_user_yolov3_example.py", line 241, in
get_calibration_table_tofa(model_path, augmented_model_path, calibration_dataset)
File "e2e_user_yolov3_example.py", line 175, in get_prediction_evaluation_yolov3_variant
write_calibration_table(calibrator.compute_range())
File "/home/lbathen/miniconda3/envs/onnx/lib/python3.8/site-packages/onnxruntime/quantization/quant_utils.py", line 409, in write_calibration_table
file.write(json.dumps(calibration_cache)) # use json.loads to do the reverse
File "/home/lbathen/miniconda3/envs/onnx/lib/python3.8/json/init.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/home/lbathen/miniconda3/envs/onnx/lib/python3.8/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/home/lbathen/miniconda3/envs/onnx/lib/python3.8/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/home/lbathen/miniconda3/envs/onnx/lib/python3.8/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.class.name} '
TypeError: Object of type float32 is not JSON serializable

--- To reproduce, download and use a different variant of Yolo, and use the get_prediction_evaluation_yolov3_variant method...

onnx==1.14.0
onnxruntime-gpu==1.14.1
cuda 12.1
ubuntu 20.04

---- Reproducible on two separate machines... ubuntu 20 and 22...