code: def __call__(self, model, param_state_dict):
      paddleslim = try_import("paddleslim")
      from paddleslim.nas.ofa import OFA, RunConfig, utils
      from paddleslim.nas.ofa.convert_super import Convert, supernet
  
      task = self.ofa_config["task"]
      expand_ratio = self.ofa_config["expand_ratio"]
  
      skip_neck = self.ofa_config["skip_neck"]
      skip_head = self.ofa_config["skip_head"]
  
      run_config = self.ofa_config["RunConfig"]
      if "skip_layers" in run_config:
          skip_layers = run_config["skip_layers"]
      else:
          skip_layers = []
  
      # supernet config
      sp_config = supernet(expand_ratio=expand_ratio)
      # convert to supernet
      model = Convert(sp_config).convert(model)
  
      skip_names = []
      if skip_neck:
          skip_names.append("neck.")
      if skip_head:
          skip_names.append("head.")
  
      for name, sublayer in model.named_sublayers():
          for n in skip_names:
              if n in name:
                  skip_layers.append(name)
  
      run_config["skip_layers"] = skip_layers
      run_config = RunConfig(**run_config)
  
      # build ofa model
      ofa_model = OFA(model, run_config=run_config)
  
      ofa_model.set_epoch(0)
      ofa_model.set_task(task)
  
      input_spec = [
          {
              "image": paddle.ones(
                  shape=[1, 3, 640, 640], dtype="float32"
              ),
              "im_shape": paddle.full([1, 2], 640, dtype="float32"),
              "scale_factor": paddle.ones(
                  shape=[1, 2], dtype="float32"
              ),
          }
      ]
  
      ofa_model._clear_search_space(input_spec=input_spec)
      ofa_model._build_ss = True
      check_ss = ofa_model._sample_config("expand_ratio", phase=None)
      # tokenize the search space
      ofa_model.tokenize()
      # check token map, search cands and search space
      logger.info("Token map is {}".format(ofa_model.token_map))
      logger.info(
          "Search candidates is {}".format(ofa_model.search_cands)
      )
      logger.info(
          "The length of search_space is {}, search_space is {}".format(
              len(ofa_model._ofa_layers), ofa_model._ofa_layers
          )
      )
      # set model state dict into ofa model
      utils.set_state_dict(ofa_model.model, param_state_dict)
      return ofa_model
  
file_path: PaddleDetection/ppdet/slim/ofa.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import load_config, merge_config, create
  from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
  from ppdet.utils.logger import setup_logger
  from ppdet.core.workspace import register, serializable
  from paddle.utils import try_import
label_desc: Use image node_id 2 for calling the OFA obj's underlying member method code with example usage: obj.__call__(model, param_state_dict) and returns: ofa_model