code: def encoder_for(args, idim, subsample):
      """Instantiates an encoder module given the program arguments
  
      :param Namespace args: The arguments
      :param int or List of integer idim: dimension of input, e.g. 83, or
                                          List of dimensions of inputs, e.g. [83,83]
      :param List or List of List subsample: subsample factors, e.g. [1,2,2,1,1], or
                                          List of subsample factors of each encoder.
                                           e.g. [[1,2,2,1,1], [1,2,2,1,1]]
      :rtype torch.nn.Module
      :return: The encoder module
      """
      num_encs = getattr(
          args, "num_encs", 1
      )  # use getattr to keep compatibility
      if num_encs == 1:
          # compatible with single encoder asr mode
          return Encoder(
              args.etype,
              idim,
              args.elayers,
              args.eunits,
              args.eprojs,
              subsample,
              args.dropout_rate,
          )
      elif num_encs >= 1:
          enc_list = torch.nn.ModuleList()
          for idx in range(num_encs):
              enc = Encoder(
                  args.etype[idx],
                  idim[idx],
                  args.elayers[idx],
                  args.eunits[idx],
                  args.eprojs,
                  subsample[idx],
                  args.dropout_rate[idx],
              )
              enc_list.append(enc)
          return enc_list
      else:
          raise ValueError(
              "Number of encoders needs to be more than one. {}".format(
                  num_encs
              )
          )
  
imports: import logging
  import numpy
  import torch
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from espnet.nets.e2e_asr_common import get_vgg2l_odim
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask, to_device
label_desc: Use image node_id 2 for calling a global function with example usage: encoder_for(args, idim, subsample) and returns: Encoder, enc_list