code: def __init__(self, name):
      self.name = name
  
      meta_info = pd.read_csv(
          os.path.join(os.path.dirname(__file__), "master.csv"),
          index_col=0,
          keep_default_na=False,
      )
      if not self.name in meta_info:
          print(self.name)
          error_mssg = "Invalid dataset name {}.\n".format(self.name)
          error_mssg += "Available datasets are as follows:\n"
          error_mssg += "\n".join(meta_info.keys())
          raise ValueError(error_mssg)
  
      self.eval_metric = meta_info[self.name]["eval metric"]
  
      if "hits@" in self.eval_metric:
          ### Hits@K
  
          self.K = int(self.eval_metric.split("@")[1])
  
file_path: ogb/ogb/linkproppred/evaluate.py
imports: import os
  import numpy
  import pandas
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 1 to create a new Evaluator object  with example: obj = Evaluator(name)