DenseNet static quantization error

I fine tuned a PyTorch DenseNet121 for binary classification and exported it as .onnx model. It works just fine with onnxruntime. Then, I followed this sample implementation https://github.com/microsoft/onnxruntime-inference-examples/blob/c9a65be0c649870a56b5d702d54b2b927de212e7/quantization/image_classification/cpu/run.py for static quantization. It creates the .quant.onnx file, but gives an error when benchmarking. Specifically, the error is thrown at session = onnxruntime.InferenceSession(model_path) call. Below is the error code:

Traceback (most recent call last):
  File "C:\venvs\pytorch10_py38\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py", line 324, in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
  File "C:\venvs\pytorch10_py38\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py", line 369, in _create_inference_session
    sess.initialize_session(providers, provider_options, disabled_optimizers)
onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Exception during initialization: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_concat.cc:35 onnxruntime::contrib::QLinearConcat::QLinearConcat input_def_count >= 8 && (input_def_count - 2) % 3 == 0 was false. At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!


Process finished with exit code 1

Is this fixable or DenseNet is not yet supported?
Thank you.

Start with quantization/image_classification/cpu/run.py from the linked ONNX Runtime example and reproduce the failure at onnxruntime.InferenceSession(model_path). Inspect the generated .quant.onnx model and the QLinearConcat error to determine whether DenseNet121 is supported. Done means establishing a reproducible resolution or documenting the support limitation.