code: def split_numpy_chunk(path, tokenizer, pretrain_data, host):
      documents = []
      instances = []
  
      s = time.time()
      with open(path, encoding="utf-8") as fd:
          document = []
          for i, line in enumerate(tqdm(fd)):
              line = line.strip()
              # document = line
              # if len(document.split("<sep>")) <= 3:
              #     continue
              if (
                  len(line) > 0 and line[:2] == "]]"
              ):  # This is end of document
                  documents.append(document)
                  document = []
              elif len(line) >= 2:
                  document.append(line)
          if len(document) > 0:
              documents.append(document)
      print("read_file ", time.time() - s)
  
      # documents = [x for x in documents if x]
      # print(len(documents))
      # print(len(documents[0]))
      # print(documents[0][0:10])
  
      ans = []
      for docs in tqdm(documents):
          ans.append(pretrain_data.tokenize(docs))
      print(time.time() - s)
      del documents
  
      instances = []
      for a in tqdm(ans):
          raw_ins = get_raw_instance(a)
          instances.extend(raw_ins)
      del ans
  
      print("len instance", len(instances))
  
      sen_num = len(instances)
      seq_len = 512
      input_ids = np.zeros([sen_num, seq_len], dtype=np.int32)
      input_mask = np.zeros([sen_num, seq_len], dtype=np.int32)
      segment_ids = np.zeros([sen_num, seq_len], dtype=np.int32)
      masked_lm_output = np.zeros([sen_num, seq_len], dtype=np.int32)
  
      for index, ins in tqdm(enumerate(instances)):
          mask_dict = pretrain_data.create_training_instance(ins)
          input_ids[index] = mask_dict[0]
          input_mask[index] = mask_dict[1]
          segment_ids[index] = mask_dict[2]
          masked_lm_output[index] = mask_dict[3]
  
      with h5py.File(f"/output/{host}.h5", "w") as hf:
          hf.create_dataset("input_ids", data=input_ids)
          hf.create_dataset("input_mask", data=input_ids)
          hf.create_dataset("segment_ids", data=segment_ids)
          hf.create_dataset(
              "masked_lm_positions", data=masked_lm_output
          )
  
      del instances
  
file_path: ColossalAI/examples/community/roberta/preprocessing/tokenize_mask.py
imports: import argparse
  import multiprocessing
  import os
  import time
  from random import shuffle
  import h5py
  import numpy
  import psutil
  from get_mask import PreTrainingDataset
  from tqdm import tqdm
  from transformers import AutoTokenizer
label_desc: Use image node_id 2 for calling a global function with example usage: split_numpy_chunk(path, tokenizer, pretrain_data, host) without return types