Runtime error when using Edge TPU with Pi 4

[tomarnison]

Hi, I am trying to get the EDGE TPU working on Raspberry Pi 4 and I am having the same issue as a few others (#44) it seems, despite updating to tflite-runtime 2.1.0.post1 and making sure I use tflite_runtime instead of tf.lite.interpreter.

Error message:

(tflite1) pi@raspberrypi:~/tflite1 $ python3 TFLite_detection_webcam.py --modeldir=mobilenet_quantized_1205 --edgetpu
INFO: Initialized TensorFlow Lite runtime.
/home/pi/tflite1/mobilenet_quantized_1205/edgetpu.tflite
Traceback (most recent call last):
  File "TFLite_detection_webcam.py", line 140, in <module>
    interpreter.allocate_tensors()
  File "/home/pi/.virtualenvs/tflite1/lib/python3.7/site-packages/tensorflow_core/lite/python/interpreter.py", line 244, in allocate_tensors
    return self._interpreter.AllocateTensors()
  File "/home/pi/.virtualenvs/tflite1/lib/python3.7/site-packages/tensorflow_core/lite/python/interpreter_wrapper/tensorflow_wrap_interpreter_wrapper.py", line 106, in AllocateTensors
    return _tensorflow_wrap_interpreter_wrapper.InterpreterWrapper_AllocateTensors(self)
RuntimeError: Internal: Unsupported data type in custom op handler: 0Node number 2 (EdgeTpuDelegateForCustomOp) failed to prepare.

Output of grep edgetpu:

(tflite1) pi@raspberrypi:~/tflite1 $ dpkg -l | grep edgetpu
ii  libedgetpu1-std:armhf                 13.0                    mhf        Support library for Edge TPU

Any Advice welcome, I'm new to this so it could be I've made a blind error somewhere.

Thanks

*Following this tutorial: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/Raspberry_Pi_Guide.md#step-1e-run-the-tensorflow-lite-model

UPDATE SOLVED***
I noticed that this line calls the tensorflow interpreter not the tflit_runtime interpreter . Given that I do have tensorflow installed I assume it was calling that interpreter, so by commenting out that option I forced it to use the runtime interpreter

pkg = importlib.util.find_spec('tensorflow')
if pkg is None:
from tflite_runtime.interpreter import Interpreter
if use_TPU:
    from tflite_runtime.interpreter import load_delegate
else:
    from tensorflow.lite.python.interpreter import Interpreter
    if use_TPU:
        from tensorflow.lite.python.interpreter import load_delegate