code: def encoder_for(
      args: Namespace,
      idim: int,
      subsample: np.ndarray,
      aux_enc_output_layers: List = [],
  ) -> torch.nn.Module:
      """Instantiate a RNN encoder with specified arguments.
  
      Args:
          args: The model arguments.
          idim: Input dimension.
          subsample: Subsampling rate per layer.
          aux_enc_output_layers: Layer IDs for auxiliary encoder output sequences.
  
      Returns:
          : Encoder module.
  
      """
      return Encoder(
          idim,
          args.etype,
          args.elayers,
          args.eunits,
          args.eprojs,
          subsample,
          dropout_rate=args.dropout_rate,
          aux_enc_output_layers=aux_enc_output_layers,
      )
  
imports: from argparse import Namespace
  from typing import List, Optional, Tuple, Union
  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, aux_enc_output_layers) and returns: Encoder