code: def __new__(cls, dataset: str):
      if dataset not in cls._instances:
          instance = super(GTMLP, cls).__new__(cls)
          instance.dataset = dataset
          if dataset == Config.Frappe:
              instance.mlp_train_path = mlp_train_frappe
              instance.mlp_score_path = mlp_score_frappe
              instance.mlp_score_path_expressflow = exp_mlp_score_frappe
              instance.mlp_score_path_weight_share = (
                  mlp_score_frappe_weight_share
              )
          elif dataset == Config.Criteo:
              instance.mlp_train_path = mlp_train_criteo
              instance.mlp_score_path = mlp_score_criteo
              instance.mlp_score_path_expressflow = exp_mlp_score_criteo
              instance.mlp_score_path_weight_share = "./not_exist"
          elif dataset == Config.UCIDataset:
              instance.mlp_train_path = mlp_train_uci_diabetes
              instance.mlp_score_path = mlp_score_uci
              instance.mlp_score_path_expressflow = exp_mlp_score_uci
              instance.mlp_score_path_weight_share = "./not_exist"
          instance.mlp_train = read_json(instance.mlp_train_path)
          instance.mlp_score = read_json(instance.mlp_score_path)
  
          # todo: here we combine two json dict, remove later
          mlp_score_expressflow = read_json(
              instance.mlp_score_path_expressflow
          )
          for arch_id in mlp_score_expressflow:
              if arch_id in instance.mlp_score:
                  instance.mlp_score[arch_id].update(
                      mlp_score_expressflow[arch_id]
                  )
  
          mlp_score_weight_share = read_json(
              instance.mlp_score_path_weight_share
          )
          for arch_id in mlp_score_weight_share:
              if arch_id in instance.mlp_score:
                  instance.mlp_score[arch_id].update(
                      {"weight_share": mlp_score_weight_share[arch_id]}
                  )
  
          instance.mlp_global_rank = generate_global_rank(
              instance.mlp_score, instance.default_alg_name_list
          )
  
          cls._instances[dataset] = instance
      return cls._instances[dataset]
  
file_path: singa/examples/model_selection/TRAILS-Database-Native-Model-Selection/internal/ml/model_selection/src/query_api/query_api_mlp.py
imports: import os
  from src.common.constant import Config
  from src.tools.compute import generate_global_rank
  from src.tools.io_tools import read_json
label_desc: Use image node_id 1 for calling the GTMLP obj's underlying member method code with example usage: obj.__new__(cls, dataset) and returns: cls