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: Add arguments for multi-speaker encoder.
docstr_tok: ["Add","arguments","for","multi-speaker","encoder","."]
code: def encoder_mix_add_arguments(parser):
      """Add arguments for multi-speaker encoder."""
      group = parser.add_argument_group(
          "E2E encoder setting for multi-speaker"
      )
      # asr-mix encoder
      group.add_argument(
          "--spa",
          action="store_true",
          help="Enable speaker parallel attention "
          "for multi-speaker speech recognition task.",
      )
      group.add_argument(
          "--elayers-sd",
          default=4,
          type=int,
          help="Number of speaker differentiate encoder layers"
          "for multi-speaker speech recognition task.",
      )
      return parser
  
imports: import argparse
  import logging
  import math
  import os
  import sys
  from itertools import groupby
  import numpy
  import torch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.e2e_asr_common import get_vgg2l_odim, label_smoothing_dist
  from espnet.nets.pytorch_backend.ctc import ctc_for
  from espnet.nets.pytorch_backend.e2e_asr import E2E
  from espnet.nets.pytorch_backend.e2e_asr import Reporter
  from espnet.nets.pytorch_backend.frontends.feature_transform import feature_transform_for
  from espnet.nets.pytorch_backend.frontends.frontend import frontend_for
  from espnet.nets.pytorch_backend.initialization import lecun_normal_init_parameters, set_forget_bias_to_one
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, make_pad_mask, pad_list, to_device, to_torch_tensor
  from espnet.nets.pytorch_backend.rnn.attentions import att_for
  from espnet.nets.pytorch_backend.rnn.decoders import decoder_for
  from espnet.nets.pytorch_backend.rnn.encoders import RNNP, VGG2L
  from espnet.nets.pytorch_backend.rnn.encoders import encoder_for
label_desc: Use image node_id 2 for calling the E2E obj's underlying member method code with example usage: obj.encoder_mix_add_arguments(parser) and returns: parser