code: def encoder_for(args, idim, subsample):
      """Construct the encoder."""
      if getattr(
          args, "use_frontend", False
      ):  # use getattr to keep compatibility
          # with frontend, the mixed speech are separated as streams for each speaker
          return encoder_for_single(args, idim, subsample)
      else:
          return EncoderMix(
              args.etype,
              idim,
              args.elayers_sd,
              args.elayers,
              args.eunits,
              args.eprojs,
              subsample,
              args.dropout_rate,
              args.num_spkrs,
          )
  
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 1 for calling a global function with example usage: encoder_for(args, idim, subsample) and returns: encoder_for_single, EncoderMix