content: batching_timeout = 5 
  instance_num = 8 
  [jpg_decoder]
  backend = "Sequential[DecodeTensor,(result2other)CvtColorTensor,PPLResizePadTensor,SyncTensor]"
  color = "bgr"
  data_format = "hwc"
  instance_num = 4
  max_h = 416
  max_w = 416
  next = "detect"
  
  [detect]
  backend = "S[TensorrtTensor,SyncTensor]"
  batching_timeout = 10
  instance_num = 2
  max = 4
  
  model = "../../../examples/yolox/yolox_tiny.onnx"
  "model::cache" = "./yolox_tiny_4.trt"
  batch_process = "CpuTensor"
  next = "post_det"
  [post_det]
  backend = "PostProcYolox" 
  instance_num = 8 
  net_h = 416 
  net_w = 416 # for PostProcYolox
  next = "sub_graph" 
  
  [sub_graph]
  map = "jpg_decoder[other:data,color:color],post_det[TASK_BOX_KEY:TASK_BOX_KEY]"
  
  backend = "MapReduce"
  jump = "cls_preprocess"
  
  merge = "score_r,score_vit,result,r18_result"
  split = "TASK_BOX_KEY"
  
  instance_num = 8
  next = "final"
  
  [final]
  
  #### sub graph ####
  [cls_preprocess]
  backend = "Sequential[PPLCropTensor,PPLResizeTensor,CvtColorTensor,NCHWTensor,SyncTensor]"
  color = "rgb"
  instance_num = 4
  resize_h = 224
  resize_w = 224
  
  next = "r18,vit"
  
  #### branch 1 ####
  [r18]
  backend = "S[TensorrtTensor,SyncTensor,ToScore]"
  batching_timeout = 10 
  instance_num = 2 
  max = 8 
  mean = "123.675, 116.28, 103.53" # 255*"0.485, 0.456, 0.406"
  model = "./resnet18.onnx" 
  "model::cache" = "./resnet18_8.trt" 
  batch_process = "CpuTensor" 
  std = "58.395, 57.120, 57.375" # 255*"0.229, 0.224, 0.225"
  
  next = "r50"
  
  [r50]
  filter = "filter_score"
  
  backend = "S[TensorrtTensor,SyncTensor,ToScore]" 
  batch_process = "CpuTensor" 
  max = 4 
  mean = "123.675, 116.28, 103.53" 
  model = "././resnet50.onnx" 
  "model::cache" = "./resnet50_4.trt" 
  std = "58.395, 57.120, 57.375" 
  
  next = "subgraph_final"
  
  #### branch 2 ####
  [vit]
  
  backend = "S[TensorrtTensor,SyncTensor,ToScore]"
  # backend = "Identity"
  
  batching_timeout = 15
  max = "16;16;16"
  batch_process = "CpuTensor"
  instance_num = 3 
  mean = "123.675, 116.28, 103.53" 
  min = "16;16;1" 
  model = "././fastervit_0_224_224.onnx" 
  "model::cache" = "./fastervit_0_224_224_16.trt" 
  next = "subgraph_final" 
  "precision::fp32" = "Softmax" 
  std = "58.395, 57.120, 57.375" 
  
  [subgraph_final]
  map = "r50[score:score_r,result:r18_result],vit[score:score_vit,result:data]"