class_docstr: E2E module.
  
  :param int idim: dimension of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
class_docstr_tok: ["E2E","module",".",":","param","int","idim",":","dimension","of","inputs",":","param","int","odim",":","dimension","of","outputs",":","param","Namespace","args",":","argument","Namespace","containing","options"]
docstr: Return PlotAttentionReport.
docstr_tok: ["Return","PlotAttentionReport","."]
code: def attention_plot_class(self):
      """Return PlotAttentionReport."""
      return PlotAttentionReport
  
imports: import logging
  import math
  from argparse import Namespace
  import numpy
  import torch
  from espnet.nets.e2e_asr_common import end_detect
  from espnet.nets.e2e_mt_common import ErrorCalculator
  from espnet.nets.mt_interface import MTInterface
  from espnet.nets.pytorch_backend.e2e_mt import Reporter
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, make_pad_mask, th_accuracy, to_device
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.argument import add_arguments_transformer_common
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder import Decoder
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.initializer import initialize
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, 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 2 for calling the E2E obj's underlying member method code with example usage: obj.attention_plot_class() and returns: PlotAttentionReport