code: def log_generation_time(self) -> None:
      logger.info(
          "Processed %d sentences (%d tokens) in %.1fs %.2f "
          "sentences per second, %.2f tokens per second)",
          self.num_sentences,
          self.gen_timer.n,
          self.gen_timer.sum,
          self.num_sentences / (self.gen_timer.sum + 1e-6),
          1.0 / (self.gen_timer.avg + 1e-6),
      )
  
imports: import ast
  import hashlib
  import logging
  import os
  import shutil
  import sys
  import re
  from dataclasses import dataclass, field, is_dataclass
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple, Union
  import editdistance
  import torch
  import torch.distributed
  from examples.speech_recognition.new.decoders.decoder_config import DecoderConfig, FlashlightDecoderConfig
  from examples.speech_recognition.new.decoders.decoder import Decoder
  from fairseq import checkpoint_utils, distributed_utils, progress_bar, tasks, utils
  from fairseq.data.data_utils import post_process
  from fairseq.dataclass.configs import CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, FairseqDataclass
  from fairseq.logging.meters import StopwatchMeter, TimeMeter
  from fairseq.logging.progress_bar import BaseProgressBar
  from fairseq.models.fairseq_model import FairseqModel
  from omegaconf import OmegaConf
  import hydra
  from hydra.core.config_store import ConfigStore
label_desc: Use image node_id 17 for calling the InferenceProcessor obj's underlying member method code with example usage: obj.log_generation_time() without return types