code: def ctc_for(args, odim, reduce=True):
      """Returns the CTC module for the given args and output dimension
  
      :param Namespace args: the program args
      :param int odim : The output dimension
      :param bool reduce : return the CTC loss in a scalar
      :return: the corresponding CTC module
      """
      num_encs = getattr(
          args, "num_encs", 1
      )  # use getattr to keep compatibility
      if num_encs == 1:
          # compatible with single encoder asr mode
          return CTC(
              odim,
              args.eprojs,
              args.dropout_rate,
              ctc_type=args.ctc_type,
              reduce=reduce,
          )
      elif num_encs >= 1:
          ctcs_list = torch.nn.ModuleList()
          if args.share_ctc:
              # use dropout_rate of the first encoder
              ctc = CTC(
                  odim,
                  args.eprojs,
                  args.dropout_rate[0],
                  ctc_type=args.ctc_type,
                  reduce=reduce,
              )
              ctcs_list.append(ctc)
          else:
              for idx in range(num_encs):
                  ctc = CTC(
                      odim,
                      args.eprojs,
                      args.dropout_rate[idx],
                      ctc_type=args.ctc_type,
                      reduce=reduce,
                  )
                  ctcs_list.append(ctc)
          return ctcs_list
      else:
          raise ValueError(
              "Number of encoders needs to be more than one. {}".format(
                  num_encs
              )
          )
  
file_path: espnet/espnet/nets/pytorch_backend/ctc.py
imports: import logging
  import numpy
  import torch
  import torch.nn.functional
  from packaging.version import parse
  from espnet.nets.pytorch_backend.nets_utils import to_device
label_desc: Use image node_id 1 for calling a global function with example usage: ctc_for(args, odim, reduce) and returns: CTC, ctcs_list