code: def test_lenet_gluon_load_params_api():
      model_name = "lenet_gluon_save_params_api"
      logging.info(f"Performing inference for model/API {model_name}")
  
      for folder in get_top_level_folders_in_bucket(
          s3, model_bucket_name
      ):
          logging.info(
              f"Fetching files for MXNet version : {folder} and model {model_name}"
          )
          model_files = download_model_files_from_s3(model_name, folder)
          if len(model_files) == 0:
              logging.warn(
                  f"No training files found for {model_name} for MXNet version : {folder}"
              )
              continue
  
          data = mx.npx.load("".join([model_name, "-data"]))
          test_data = data["data"]
          # Load the model and perform inference
          loaded_model = Net()
          loaded_model.load_params(model_name + "-params")
          output = loaded_model(test_data)
          old_inference_results = mx.npx.load(
              model_name + "-inference"
          )["inference"]
          assert_almost_equal(
              old_inference_results.asnumpy(),
              output.asnumpy(),
              rtol=rtol_default,
              atol=atol_default,
          )
          clean_model_files(model_files, model_name)
          logging.info("=================================")
      logging.info(f"Assertion passed for model : {model_name}")
  
imports: from .common import *
label_desc: Use image node_id 1 for calling a global function with example usage: test_lenet_gluon_load_params_api() without return types