code: def decode_summary(summary_tokens, tokenizer):
      """Decode the summary and return it in a format
      suitable for evaluation.
      """
      summary_tokens = summary_tokens.to("cpu").numpy()
      summary = tokenizer.decode(summary_tokens)
      sentences = summary.split(".")
      sentences = [s + "." for s in sentences]
      return sentences
  
imports: import argparse
  import logging
  import os
  import sys
  from collections import namedtuple
  import torch
  from torch.utils.data import DataLoader, SequentialSampler
  from tqdm import tqdm
  from modeling_bertabs import BertAbs, build_predictor
  from transformers import BertTokenizer
  from .utils_summarization import CNNDMDataset, build_mask, compute_token_type_ids, encode_for_summarization, truncate_or_pad
label_desc: Use image node_id 9 for calling a global function with example usage: decode_summary(summary_tokens, tokenizer) and returns: sentences