code: def load_meta(self, test_data_path):
      # Load meta files
      datas = {}
      if not os.listdir(test_data_path):
          raise ValueError("Empty test_data_path")
      bin_files_found = False
      print("Reading ", end="")
      for file_name in os.listdir(test_data_path):
          if ".bin" not in file_name:
              continue
          bin_files_found = True
          print(file_name, end=", ")
          with open(test_data_path + file_name, "rb") as f:
              datas[file_name.split(".")[0]] = pickle.load(f)
      if not bin_files_found:
          raise ValueError(f"Can't find bin files in {test_data_path}")
      print("Done")
      self.m_loc = datas[FEATURES_NAMES[9]]
      self.tokens = datas[FEATURES_NAMES[10]]
      self.lookup = datas[FEATURES_NAMES[11]]
      self.docs = datas[FEATURES_NAMES[12]]
      self.flat_m_idx = list(
          (doc_i, m_i)
          for doc_i, l in enumerate(self.m_loc)
          for m_i in range(len(l))
      )
  
imports: import os
  import subprocess
  import io
  import pickle
  import torch
  from torch.utils.data import DataLoader
  from neuralcoref.train.conllparser import FEATURES_NAMES
  from neuralcoref.train.dataset import NCBatchSampler, padder_collate
  from neuralcoref.train.compat import unicode_
label_desc: Use image node_id 2 for calling the ConllEvaluator obj's underlying member method code with example usage: obj.load_meta(test_data_path) without return types