docstr: Parameters
  ----------
  output_dir
      The directory to save predictions.
  write_interval
      When to write. Could be "batch" or "epoch".
      See Lightning's source code at
      https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.BasePredictionWriter.html
  sleep_time
      If other process does not finish writing, sleep for a few seconds and recheck.
docstr_tok: ["Parameters","--","--","--","--","--","output_dir","The","directory","to","save","predictions",".","write_interval","When","to","write",".","Could","be","``","batch","''","or","``","epoch","''",".","See","Lightning","'s","source","code","at","https",":","\/\/lightning.ai\/docs\/pytorch\/stable\/api\/lightning.pytorch.callbacks.BasePredictionWriter.html","sleep_time","If","other","process","does","not","finish","writing",",","sleep","for","a","few","seconds","and","recheck","."]
code: def __init__(
      self,
      output_dir: Optional[str],
      write_interval: Optional[str],
      strategy: Optional[str],
      sleep_time=5,
  ):
      """
      Parameters
      ----------
      output_dir
          The directory to save predictions.
      write_interval
          When to write. Could be "batch" or "epoch".
          See Lightning's source code at
          https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.BasePredictionWriter.html
      sleep_time
          If other process does not finish writing, sleep for a few seconds and recheck.
      """
      super().__init__(write_interval)
      if (
          output_dir is None
      ):  # TODO: give the predictor a default save_path before calling DDPPredictionWriter
          output_dir = "temp_cache_for_ddp_prediction"
          logging.warning(
              f"Current predictor's save_path is None, using a default cache folder which may cause an error in prediction I/O. Try init the predictor with a save_path."
          )
      assert isinstance(
          output_dir, (str, Path)
      ), f"Only str and pathlib.Path types are supported for path, but got {output_dir} of type {type(output_dir)}."
      self.sleep_time = sleep_time
      output_dir = os.path.abspath(os.path.expanduser(output_dir))
      if "spawn" in strategy:
          self.output_dir = os.path.join(
              output_dir, f"predictions_{uuid.uuid4().hex}"
          )
      else:
          self.output_dir = os.path.join(
              output_dir, "ddp_prediction_cache"
          )
      try:
          os.makedirs(self.output_dir, exist_ok=False)
      except FileExistsError:  # assume the string is unique
          logger.warning(
              f"{self.output_dir} already exists. This could be caused by DDP subprocess. Just make sure the previous cache is removed in {self.output_dir}."
          )
  
imports: import logging
  import os
  import shutil
  import time
  import uuid
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence
  import lightning.pytorch
  import torch
  from lightning.pytorch.callbacks import BasePredictionWriter
  from ..constants import BBOX, LM_TARGET, LOGIT_SCALE, LOGITS, TEMPLATE_LOGITS, WEIGHT
label_desc: Use image node_id 1 to create a new DDPPredictionWriter object from inherited base classes: BasePredictionWriter with example: obj = DDPPredictionWriter(output_dir, write_interval, strategy, sleep_time)