code: import argparse
  import os
  
  import torch
  from tinynn.converter import TFLiteConverter
  from tinynn.util.converter_util import export_converter_files, parse_config
  
  CURRENT_PATH = os.path.abspath(os.path.dirname(__file__))
  
  
  def export_files():
      from models.cifar10.mobilenet import DEFAULT_STATE_DICT, Mobilenet
  
      model = Mobilenet()
      model.load_state_dict(torch.load(DEFAULT_STATE_DICT))
      model.cpu()
      model.eval()
  
      dummy_input = torch.rand((1, 3, 224, 224))
  
      export_dir = 'out'
      export_name = 'mbv1_224'
  
      export_converter_files(model, dummy_input, export_dir, export_name)
  
      json_file = os.path.join(CURRENT_PATH, export_dir, f'{export_name}.json')
      return json_file
  
  
  def main_worker(args):
      json_file = args.path
      if json_file is None:
          json_file = export_files()
  
      # We will try to parse the config and prepare the inputs for you.
      # If you want to use your own inputs, just assign it to `generated_inputs` here.
      torch_model_path, tflite_model_path, input_transpose, generated_inputs, output_transpose = parse_config(json_file)
  
      # When converting quantized models, please ensure the quantization backend is set.
      torch.backends.quantized.engine = 'qnnpack'
  
      with torch.no_grad():
          model = torch.jit.load(torch_model_path)
          model.cpu()
          model.eval()
  
          # Pay attention to the arguments `input_transpose` and `output_transpose` in the next line.
          # By default, we will perform nchw -> nhwc transpose every 4D input and output tensor.
          # If you don't want to do this, please pass in False for them.
          converter = TFLiteConverter(model, generated_inputs, tflite_model_path, input_transpose, output_transpose)
          converter.convert()
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--path', metavar='DIR', default=None, help='path to the config (.json)')
  
      args = parser.parse_args()
      main_worker(args)
  
apis: torch.jit.load torch.rand torch.no_grad torch.load