code: def get_encoder_decoder_hp(model="gin", decoder=None):
      if model == "gin":
          model_hp = {
              "num_layers": 5,
              "hidden": [64, 64, 64, 64],
              "dropout": 0.5,
              "act": "relu",
              "eps": "False",
              "mlp_layers": 2,
              "neighbor_pooling_type": "sum",
          }
      elif model == "gat":
          model_hp = {
              # hp from model
              "num_layers": 2,
              "hidden": [8],
              "heads": 8,
              "dropout": 0.6,
              "act": "elu",
          }
      elif model == "gcn":
          model_hp = {
              "num_layers": 2,
              "hidden": [16],
              "dropout": 0.5,
              "act": "relu",
          }
      elif model == "sage":
          model_hp = {
              "num_layers": 2,
              "hidden": [64],
              "dropout": 0.5,
              "act": "relu",
              "agg": "mean",
          }
      elif model == "topk":
          model_hp = {"num_layers": 5, "hidden": [64, 64, 64, 64]}
  
      if decoder is None:
          decoder_hp = {
              "hidden": 64,
              "dropout": 0.5,
              "act": "relu",
              "graph_pooling_type": "sum",
          }
      elif decoder == "JKSumPoolMLP":
          decoder_hp = {"dropout": 0.5, "graph_pooling_type": "sum"}
      elif decoder == "topk":
          decoder_hp = {"dropout": 0.5}
  
      return model_hp, decoder_hp
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_encoder_decoder_hp(model, decoder) and returns: model_hp,  decoder_hp