class_docstr: images - leave them in original dimension (unpadded), but do normalize them.
          Should be CPU tensors of shape B*T*3*H*W
  
  mem_profile - How extravagant I can use the GPU memory.
              Usually more memory -> faster speed but I have not drawn the exact relation
              0 - Use the most memory
              1 - Intermediate, larger buffer
              2 - Intermediate, small buffer
              3 - Use the minimal amount of GPU memory
              Note that *none* of the above options will affect the accuracy
              This is a space-time tradeoff, not a space-performance one
  
  mem_freq - Period at which new memory are put in the bank
              Higher number -> less memory usage
              Unlike the last option, this *is* a space-performance tradeoff
class_docstr_tok: ["images","-","leave","them","in","original","dimension","(","unpadded",")",",","but","do","normalize","them",".","Should","be","CPU","tensors","of","shape","B","*","T","*","3","*","H","*","W","mem_profile","-","How","extravagant","I","can","use","the","GPU","memory",".","Usually","more","memory","-",">","faster","speed","but","I","have","not","drawn","the","exact","relation","0","-","Use","the","most","memory","1","-","Intermediate",",","larger","buffer","2","-","Intermediate",",","small","buffer","3","-","Use","the","minimal","amount","of","GPU","memory","Note","that","*","none","*","of","the","above","options","will","affect","the","accuracy","This","is","a","space-time","tradeoff",",","not","a","space-performance","one","mem_freq","-","Period","at","which","new","memory","are","put","in","the","bank","Higher","number","-",">","less","memory","usage","Unlike","the","last","option",",","this","*","is","*","a","space-performance","tradeoff"]
code: def set_model(self, param_path=None):
      if param_path is None or not os.path.exists(param_path):
          raise Exception(f"权重路径{param_path}不存在。请指定正确的模型路径")
  
      # param path
      param_path = os.path.abspath(os.path.dirname(param_path))
      # **************memorize**********************
      memory_model_path = os.path.join(
          param_path, "static_propagation_memorize.pdmodel"
      )
      memory_param_path = os.path.join(
          param_path, "static_propagation_memorize.pdiparams"
      )
      self.prop_net_memory = load_model(
          memory_model_path, memory_param_path
      )
      # **************segmentation**********************
      segment_model_path = os.path.join(
          param_path, "static_propagation_segment.pdmodel"
      )
      segment_param_path = os.path.join(
          param_path, "static_propagation_segment.pdiparams"
      )
      self.prop_net_segm = load_model(
          segment_model_path, segment_param_path
      )
      # **************attention**********************
      attn_model_path = os.path.join(
          param_path, "static_propagation_attention"
      )
      self.prop_net_attn = jit_load(attn_model_path)
      # **************fusion**********************
      fusion_model_path = os.path.join(
          param_path, "static_fusion.pdmodel"
      )
      fusion_param_path = os.path.join(
          param_path, "static_fusion.pdiparams"
      )
      self.fuse_net = load_model(fusion_model_path, fusion_param_path)
  
      return True, "模型设置成功"
  
imports: import os
  import paddle.nn.functional
  import numpy
  from .load_model import *
  from .util.tensor_util import pad_divide_by, images_to_paddle
  from .video_tools import load_video, aggregate_wbg
label_desc: Use image node_id 6 for calling the InferenceCore obj's underlying member method code with example usage: obj.set_model(param_path) and returns: True,  str