class_docstr: Writes model logits to a file.
  
  The class is designed for use in an asynchronous process spawned by torch.multiprocessing.spawn, e.g.
    logits_writer = LogitsWriter(...)
    logits_writer_ctx = torch.multiprocessing.spawn(logits_writer.run, join=False)
    logits_writer_ctx.join()
class_docstr_tok: ["Writes","model","logits","to","a","file",".","The","class","is","designed","for","use","in","an","asynchronous","process","spawned","by","torch.multiprocessing.spawn",",","e.g",".","logits_writer","=","LogitsWriter","(","...",")","logits_writer_ctx","=","torch.multiprocessing.spawn","(","logits_writer.run",",","join=False",")","logits_writer_ctx.join","(",")"]
docstr: We must open the file in binary model for gzip
docstr_tok: ["We","must","open","the","file","in","binary","model","for","gzip"]
code: def _get_open_options(self):
      """We must open the file in binary model for gzip"""
      if self.use_gzip:
          return {"mode": "wb"}
      else:
          return {"mode": "w", "encoding": "utf-8"}
  
imports: import gzip
  import json
  import os
  import sys
  import traceback
  from typing import Any, Dict, get_type_hints, IO, Iterator, List, Optional, Tuple, Union
  import torch
  from pytext.common.constants import Stage
  from pytext.config import ExportConfig, PyTextConfig, TestConfig
  from pytext.config.component import ComponentType, create_component, create_exporter
  from pytext.data.data import Batcher
  from pytext.data.data_handler import CommonMetadata
  from pytext.metric_reporters.channel import Channel
  from pytext.task import create_task, get_latest_checkpoint_path, load, NewTask, save, Task_Deprecated
  from pytext.task.disjoint_multitask import NewDisjointMultitask
  from pytext.trainers import TrainingState
  from pytext.utils import cuda, distributed, precision, round_seq, set_random_seeds, timing
  from pytext.utils.file_io import PathManager
label_desc: Use image node_id 3 for calling the LogitsWriter obj's underlying member method code with example usage: obj._get_open_options() and returns: dict, dict