code: def __init__(self, config, language="english"):
      self.config = config
      self.language = language
  
      self.max_seg_len = config["max_segment_len"]
  
      self.data_dir = config["data_dir"]
      self.dataset = config["dataset"]
  
      # Get tensorized samples
      cache_path = self.get_cache_path()
      if os.path.exists(cache_path):
          # Load cached tensors if exists
          self.tensor_samples, self.stored_info = pickle.load(
              open(cache_path, "rb")
          )
          logger.info(
              f"Loaded tensorized examples from cache: {cache_path}"
          )
      else:
          # Generate tensorized samples
          if self.dataset == "ontonotes_coref":
              self.tensor_samples = {}
              tensorizer = Tensorizer(self.config)
              suffix = f'{self.config["plm_tokenizer_name"]}.{language}.{self.max_seg_len}.jsonlines'
  
              paths = {
                  "train": join(self.data_dir, f"train.{suffix}"),
                  "dev": join(self.data_dir, f"dev.{suffix}"),
                  "test": join(self.data_dir, f"test.{suffix}"),
              }
  
          if (
              "t5-small" == self.config["plm_tokenizer_name"]
          ):  # use full-size data for evaluation
              suffix = f'{self.config["plm_tokenizer_name"]}.{language}.4096.jsonlines'
              paths["dev"] = join(self.data_dir, f"dev.{suffix}")
              paths["test"] = join(self.data_dir, f"test.{suffix}")
  
          for split, path in paths.items():
              logger.info(
                  f"Tensorizing examples from {path}; results will be cached)"
              )
              is_training = split == "train"
  
              samples = json.load(open(path))
              tensor_samples = [
                  tensorizer.tensorize_example(sample, is_training)
                  for sample in samples
              ]
  
              self.tensor_samples[split] = CorefDataset(
                  sorted(
                      [
                          (doc_key, tensor)
                          for doc_key, tensor in tensor_samples
                      ],
                      key=lambda x: -x[1]["input_ids"].size(0),
                  )
              )
          self.stored_info = tensorizer.stored_info
          # Cache tensorized samples
          pickle.dump(
              (self.tensor_samples, self.stored_info),
              open(cache_path, "wb"),
          )
          logger.info(f"Cached tensorized examples to {cache_path}")
  
imports: import sys
  import os
  import numpy
  import random
  from transformers import T5Tokenizer
  import os
  from os.path import join
  import json
  import pickle
  import logging
  import torch
  import copy
  import torch
  from torch.utils.data import Dataset, DataLoader
  import torch.nn.functional
  import util
label_desc: Use image node_id 1 to create a new CorefDataProcessor object  with example: obj = CorefDataProcessor(config, language)