Predictions of custom converted YOLOv4 model

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Hi. I'm new to inference using ONNX and I'm getting troubles to use the predictions I get using my custom YOLOv4 model, recently converted thanks to this notebook.

Then, I wanted to use the notebook for inference, but I'm not getting the same kind of output. Instead of something like [(1, 52, 52, 3, 85), (1, 26, 26, 3, 85), (1, 13, 13, 3, 85)], I get [(1, 46, 15)].
Next, I get the following error using the postprocess_bbox function:

<ipython-input-3-265b08cda32c> in postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE)
     28         conv_shape = pred.shape
     29         output_size = conv_shape[1]
---> 30         conv_raw_dxdy = pred[:, :, :, :, 0:2]
     31         conv_raw_dwdh = pred[:, :, :, :, 2:4]
     32         xy_grid = np.meshgrid(np.arange(output_size), np.arange(output_size))

IndexError: too many indices for array: array is 3-dimensional, but 5 were indexed

Of course, it does not have the same shape... But I don't understand the meaning of the shape I obtain, knowing that my model output 11 classes and the input resolution is 416, just like the example. In case it helps, my output_names is : [tf.concat_16]

In my custom model, I did not change the anchors, the strides nor the xy scales, it was simply trained with the default values.
Does anyone know the meaning of the shape I get or what's wrong with my model to obtain such a shape?

Is this issue related to a specific model?

Model name (e.g. mnist): YOLOv4 (custom)
Model opset (e.g. 7): 11 (default in the notebook)

Start with dependencies/Conversion.ipynb and dependencies/inference.ipynb, then inspect the custom model's output_names and tensor shape before postprocess_bbox. Compare the converted custom model with the example's expected output tensors; done means the shape is explained and the inference/postprocessing path is shown to be compatible or the incompatibility is documented.