class_docstr: A chainer reporter wrapper for Transducer models.
class_docstr_tok: ["A","chainer","reporter","wrapper","for","Transducer","models","."]
docstr: Instantiate reporter attributes.
  
  Args:
      loss: Model loss.
      loss_trans: Main Transducer loss.
      loss_ctc: CTC loss.
      loss_aux_trans: Auxiliary Transducer loss.
      loss_symm_kl_div: Symmetric KL-divergence loss.
      loss_lm: Label smoothing loss.
      cer: Character Error Rate.
      wer: Word Error Rate.
docstr_tok: ["Instantiate","reporter","attributes",".","Args",":","loss",":","Model","loss",".","loss_trans",":","Main","Transducer","loss",".","loss_ctc",":","CTC","loss",".","loss_aux_trans",":","Auxiliary","Transducer","loss",".","loss_symm_kl_div",":","Symmetric","KL-divergence","loss",".","loss_lm",":","Label","smoothing","loss",".","cer",":","Character","Error","Rate",".","wer",":","Word","Error","Rate","."]
code: def report(
      self,
      loss: float,
      loss_trans: float,
      loss_ctc: float,
      loss_aux_trans: float,
      loss_symm_kl_div: float,
      loss_lm: float,
      cer: float,
      wer: float,
  ):
      """Instantiate reporter attributes.
  
      Args:
          loss: Model loss.
          loss_trans: Main Transducer loss.
          loss_ctc: CTC loss.
          loss_aux_trans: Auxiliary Transducer loss.
          loss_symm_kl_div: Symmetric KL-divergence loss.
          loss_lm: Label smoothing loss.
          cer: Character Error Rate.
          wer: Word Error Rate.
  
      """
      chainer.reporter.report({"loss": loss}, self)
      chainer.reporter.report({"loss_trans": loss_trans}, self)
      chainer.reporter.report({"loss_ctc": loss_ctc}, self)
      chainer.reporter.report({"loss_lm": loss_lm}, self)
      chainer.reporter.report({"loss_aux_trans": loss_aux_trans}, self)
      chainer.reporter.report(
          {"loss_symm_kl_div": loss_symm_kl_div}, self
      )
      chainer.reporter.report({"cer": cer}, self)
      chainer.reporter.report({"wer": wer}, self)
  
      logging.info("loss:" + str(loss))
  
imports: import logging
  import math
  from argparse import ArgumentParser, Namespace
  from dataclasses import asdict
  from typing import List
  import chainer
  import numpy
  import torch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, make_non_pad_mask
  from espnet.nets.pytorch_backend.transducer.arguments import add_auxiliary_task_arguments, add_custom_decoder_arguments, add_custom_encoder_arguments, add_custom_training_arguments, add_decoder_general_arguments, add_encoder_general_arguments, add_rnn_decoder_arguments, add_rnn_encoder_arguments, add_transducer_arguments
  from espnet.nets.pytorch_backend.transducer.custom_decoder import CustomDecoder
  from espnet.nets.pytorch_backend.transducer.custom_encoder import CustomEncoder
  from espnet.nets.pytorch_backend.transducer.error_calculator import ErrorCalculator
  from espnet.nets.pytorch_backend.transducer.initializer import initializer
  from espnet.nets.pytorch_backend.transducer.rnn_decoder import RNNDecoder
  from espnet.nets.pytorch_backend.transducer.rnn_encoder import encoder_for
  from espnet.nets.pytorch_backend.transducer.transducer_tasks import TransducerTasks
  from espnet.nets.pytorch_backend.transducer.utils import get_decoder_input, valid_aux_encoder_output_layers
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention, RelPositionMultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.mask import target_mask
  from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 1 for calling the Reporter obj's underlying member method code with example usage: obj.report(loss, loss_trans, loss_ctc, loss_aux_trans, loss_symm_kl_div, loss_lm, cer, wer) without return types