class_docstr: Dummy module to use chainer's trainer.
class_docstr_tok: ["Dummy","module","to","use","chainer","'s","trainer","."]
docstr: Report nothing.
docstr_tok: ["Report","nothing","."]
code: def report(self, loss):
      """Report nothing."""
      pass
  
imports: import copy
  import json
  import logging
  import numpy
  import torch
  import torch.nn
  from chainer import Chain, reporter, training
  from chainer.dataset import convert
  from chainer.training import extensions
  from torch.nn.parallel import data_parallel
  from espnet.asr.asr_utils import snapshot_object, torch_load, torch_resume, torch_snapshot
  from espnet.lm.lm_utils import MakeSymlinkToBestModel, ParallelSentenceIterator, count_tokens, load_dataset, read_tokens
  from espnet.nets.lm_interface import LMInterface, dynamic_import_lm
  from espnet.optimizer.factory import dynamic_import_optimizer
  from espnet.scheduler.pytorch import PyTorchScheduler
  from espnet.scheduler.scheduler import dynamic_import_scheduler
  from espnet.utils.deterministic_utils import set_deterministic_pytorch
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
label_desc: Use image node_id 1 for calling the Reporter obj's underlying member method code with example usage: obj.report(loss) without return types