mobilenet_v1_1.0_224_quant.tflite vs mobilenet_v1_1.0_224.tflite for inference

In "Identify objects in images using custom machine learning models with ML Kit for Firebase" tutorial https://codelabs.developers.google.com/codelabs/mlkit-android-custom-model/index.html?index=..%2F..index#1

There is a step to unpack the downloaded zip file. This will unpack a root folder (mobilenet_v1_1.0_224_quant) inside which you will find the Tensor Flow Lite custom model we will use in this codelab (mobilenet_v1_1.0_224_quant.tflite).

It looks like mobilenet_v1_1.0_224_quant.tflite can run inference with no problem. However, If I download mobilenet_v1_1.0_224.tflite from mobilenet_v1_1.0_224 https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md, Inference get stuck at
val inferenceOutput = it.result?.getOutput>(0)!!

Is there a reason why mobilenet_v1_1.0_224.tflite does not work for the current code base for inference and how to update the current code base to get mobilenet_v1_1.0_224.tflite working?

Start with the inference code in the linked ML Kit for Firebase Android codelab, especially the call to getOutput>(0), and compare it with the two MobileNet TFLite model variants. Reproduce the stall using mobilenet_v1_1.0_224.tflite and determine the expected output type and model compatibility. Done means the codelab explains the difference and the non-quantized model can complete inference, or clearly documents why it is unsupported.

Written by the indexing model from the issue text.