def METHOD_NAME(model_path, x_values):
  # TFLite interpreter
  tflite_interpreter = tf.lite.Interpreter(
      model_path=model_path,
      experimental_op_resolver_type=tf.lite.experimental.OpResolverType.
      BUILTIN_REF,
  )
  tflite_interpreter.allocate_tensors()
  input_details = tflite_interpreter.get_input_details()[0]
  output_details = tflite_interpreter.get_output_details()[0]
  input_shape = np.array(input_details.get('shape'))
  y_predictions = np.empty(x_values.size, dtype=np.float32)
  for i, x_value in enumerate(x_values):
    y_predictions[i] = invoke_tflite_interpreter(
        input_shape,
        tflite_interpreter,
        x_value,
        input_details['index'],
        output_details['index'],
    )
  return y_predictions