code: def convert_melgan_to_tflite(
      model, output_path=None, experimental_converter=True
  ):
      """Convert Tensorflow MelGAN model to TFLite. Save a binary file if output_path is
      provided, else return TFLite model."""
  
      concrete_function = model.inference_tflite.get_concrete_function()
      converter = tf.lite.TFLiteConverter.from_concrete_functions(
          [concrete_function]
      )
      converter.experimental_new_converter = experimental_converter
      converter.optimizations = []
      converter.target_spec.supported_ops = [
          tf.lite.OpsSet.TFLITE_BUILTINS,
          tf.lite.OpsSet.SELECT_TF_OPS,
      ]
      tflite_model = converter.convert()
      print(
          f"Tflite Model size is {len(tflite_model) / (1024.0 * 1024.0)} MBs."
      )
      if output_path is not None:
          # same model binary if outputpath is provided
          with open(output_path, "wb") as f:
              f.write(tflite_model)
          return None
      return tflite_model
  
label_desc: Use image node_id 1 for calling a global function with example usage: convert_melgan_to_tflite(model, output_path, experimental_converter) and returns: tflite_model, None