code: def skipped_test_tflite_runtime():
      def get_tflite_model_path(target_edgetpu):
          # Return a path to the model
          edgetpu_path = os.getenv(
              "EDGETPU_PATH", "/home/mendel/edgetpu"
          )
          # Obtain mobilenet model from the edgetpu repo path
          if target_edgetpu:
              model_path = os.path.join(
                  edgetpu_path,
                  "test_data/mobilenet_v1_1.0_224_quant_edgetpu.tflite",
              )
          else:
              model_path = os.path.join(
                  edgetpu_path,
                  "test_data/mobilenet_v1_1.0_224_quant.tflite",
              )
          return model_path
  
      def init_interpreter(model_path, target_edgetpu):
          # Initialize interpreter
          if target_edgetpu:
              edgetpu_path = os.getenv(
                  "EDGETPU_PATH", "/home/mendel/edgetpu"
              )
              libedgetpu = os.path.join(
                  edgetpu_path,
                  "libedgetpu/direct/aarch64/libedgetpu.so.1",
              )
              interpreter = tflite.Interpreter(
                  model_path=model_path,
                  experimental_delegates=[
                      tflite.load_delegate(libedgetpu)
                  ],
              )
          else:
              interpreter = tflite.Interpreter(model_path=model_path)
          return interpreter
  
      def check_remote(server, target_edgetpu=False):
          tflite_model_path = get_tflite_model_path(target_edgetpu)
  
          # inference via tflite interpreter python apis
          interpreter = init_interpreter(
              tflite_model_path, target_edgetpu
          )
          interpreter.allocate_tensors()
          input_details = interpreter.get_input_details()
          output_details = interpreter.get_output_details()
  
          input_shape = input_details[0]["shape"]
          tflite_input = np.array(
              np.random.random_sample(input_shape), dtype=np.uint8
          )
          interpreter.set_tensor(
              input_details[0]["index"], tflite_input
          )
          interpreter.invoke()
          tflite_output = interpreter.get_tensor(
              output_details[0]["index"]
          )
  
          # inference via remote tvm tflite runtime
          remote = rpc.connect(server.host, server.port)
          dev = remote.cpu(0)
          if target_edgetpu:
              runtime_target = "edge_tpu"
          else:
              runtime_target = "cpu"
  
          with open(tflite_model_path, "rb") as model_fin:
              runtime = tflite_runtime.create(
                  model_fin.read(), dev, runtime_target
              )
              runtime.set_input(0, tvm.nd.array(tflite_input, dev))
              runtime.invoke()
              out = runtime.get_output(0)
              np.testing.assert_equal(out.numpy(), tflite_output)
  
      # Target CPU on coral board
      check_remote(rpc.Server("127.0.0.1"))
      # Target EdgeTPU on coral board
      check_remote(rpc.Server("127.0.0.1"), target_edgetpu=True)
  
imports: import os
  import tvm
  from tvm import te
  import numpy
  from tvm import rpc
  from tvm.contrib import utils, tflite_runtime
label_desc: Use image node_id 1 for calling a global function with example usage: skipped_test_tflite_runtime() and returns: model_path, interpreter