code: def run_tflite_model(model_path, image_data):
      #
      # Load TFLite model and allocate tensors.
      #
      interpreter = tf.lite.Interpreter(model_path=model_path)
      interpreter.allocate_tensors()
      #
      # Get input and output tensors.
      #
      input_details = interpreter.get_input_details()[0]
      output_details = interpreter.get_output_details()[0]
  
      #
      # Run test images
      #
      tf_results = np.empty(shape=[NUM_ITERATIONS, 10], dtype=np.float)
      for i, image in enumerate(image_data):
          #
          # Normalize the input data
          #
          image = image / 255.0
          image = scale_input_data(input_details, image)
          interpreter.set_tensor(input_details["index"], image)
          interpreter.invoke()
          tf_results[i] = interpreter.get_tensor(
              output_details["index"]
          )
          tf_results[i] = scale_output_data(
              output_details, tf_results[i]
          )
  
          print(f"== [{i}] TFLite Output:")
          print(tf_results[i])
  
      return tf_results
  
imports: import os
  import shutil
  import struct
  import sys
  import numpy
  import tensorflow
  import tvm
  import tvm.relay
  from tvm.micro.contrib import stm32
  from tvm.contrib.download import download_testdata
  from tvm import testing
  import conftest
label_desc: Use image node_id 8 for calling a global function with example usage: run_tflite_model(model_path, image_data) and returns: tf_results