code: def _get_context(center_idx, sentence_boundaries, window_size,
                   random_window_size, seed):
      
      random.seed(seed + center_idx)
  
      sentence_index = np.searchsorted(sentence_boundaries, center_idx)
      sentence_start, sentence_end = _get_sentence_start_end(
          sentence_boundaries, sentence_index)
  
      if random_window_size:
          window_size = random.randint(1, window_size)
      start_idx = max(sentence_start, center_idx - window_size)
      end_idx = min(sentence_end, center_idx + window_size + 1)
  
      if start_idx != center_idx and center_idx + 1 != end_idx:
          context = np.concatenate((np.arange(start_idx, center_idx),
                                    np.arange(center_idx + 1, end_idx)))
      elif start_idx != center_idx:
          context = np.arange(start_idx, center_idx)
      elif center_idx + 1 != end_idx:
          context = np.arange(center_idx + 1, end_idx)
      else:
          context = None
  
      return context
docs: Compute the context with respect to a center word in a sentence.
  
      Takes an numpy array of sentences boundaries.