If your final purpose is to use SNPE, try to quantize with their tools, exporting the float model with tflite and then transform it to their .dlc format.
converter = tf.lite.TFLiteConverter.from_keras_model(model) # model is a tf.keras.Model 
tflite_model = converter.convert()
with open(tflite_path, 'wb') as f:
    f.write(tflite_model)


You could also quantize with tensorflow lite and perform QAT converting the final model to tflite, however Qualcomm seems to not support yet many stuff for tflite (if you try full-integer quantization it will give you a bunch of problems).   Maybe check the version 1.53.2 Snapdragon Neural Processing Engine SDK: Revision History 4
also just as a tip, try to look your network in the netron app, that will give you some hints about the inputs and how the layers are working inside the model