code: def load_params(self, dirname, from_one_head):
      if from_one_head:
          logger.info("[From one head, extend to multi head:]")
          # load model 0
          fluid.io.load_params(
              executor=self.fluid_executor,
              dirname=dirname,
              main_program=self.learn_programs[0],
          )
  
          # sync identity params of model/target_model 0 to other models/target_models
          for i in range(1, self.ensemble_num):
              params = list(
                  filter(
                      lambda x: "identity" in x.name
                      and "@GRAD" not in x.name,
                      self.learn_programs[i].list_vars(),
                  )
              )
              for param in params:
                  param_var = _fetch_var(param.name, return_numpy=False)
  
                  model0_name = re.sub(
                      r"identity_\d+", "identity_0", param.name
                  )
                  model0_value = _fetch_var(
                      model0_name, return_numpy=True
                  )
                  logger.info(
                      "{} -> {}".format(model0_name, param.name)
                  )
                  param_var.set(model0_value, self.place)
  
          # sync share params of target_model 0 to other target models
          # After deepcopy, shapre params between target models  is different
          for i in range(1, self.ensemble_num):
              params = list(
                  filter(
                      lambda x: "shared" in x.name
                      and "PARL_target" in x.name
                      and "@GRAD" not in x.name,
                      self.learn_programs[i].list_vars(),
                  )
              )
              for param in params:
                  param_var = _fetch_var(param.name, return_numpy=False)
  
                  model0_name = re.sub(r"_\d+$", "_0", param.name)
                  model0_value = _fetch_var(
                      model0_name, return_numpy=True
                  )
                  logger.info(
                      "{} -> {}".format(model0_name, param.name)
                  )
                  param_var.set(model0_value, self.place)
  
      else:
          for i in range(self.ensemble_num):
              fluid.io.load_params(
                  executor=self.fluid_executor,
                  dirname=dirname,
                  main_program=self.learn_programs[i],
              )
  
file_path: PARL/examples/NeurIPS2018-AI-for-Prosthetics-Challenge/opensim_agent.py
imports: import numpy
  import re
  import parl
  from parl import layers
  from paddle import fluid
  from paddle.fluid.executor import _fetch_var
  from parl.utils import logger
label_desc: Use image node_id 7 for calling the OpenSimAgent obj's underlying member method code with example usage: obj.load_params(dirname, from_one_head) without return types