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: Forward only in the frontend stage.
  
  :param ndarray xs: input acoustic feature (T, C, F)
  :return: enhaned feature
  :rtype: torch.Tensor
code: def enhance(self, xs):
      """Forward only in the frontend stage.
  
      :param ndarray xs: input acoustic feature (T, C, F)
      :return: enhaned feature
      :rtype: torch.Tensor
      """
      if self.frontend is None:
          raise RuntimeError("Frontend does't exist")
      prev = self.training
      self.eval()
      ilens = np.fromiter((xx.shape[0] for xx in xs), dtype=np.int64)
  
      # subsample frame
      xs = [xx[:: self.subsample[0], :] for xx in xs]
      xs = [to_device(self, to_torch_tensor(xx).float()) for xx in xs]
      xs_pad = pad_list(xs, 0.0)
      enhanced, hlensm, mask = self.frontend(xs_pad, ilens)
      if prev:
          self.train()
      return enhanced.cpu().numpy(), mask.cpu().numpy(), ilens
  
imports: import argparse
  import logging
  import math
  import os
  from itertools import groupby
  import chainer
  import numpy
  import torch
  from chainer import reporter
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.pytorch_backend.ctc import ctc_for
  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, pad_list, to_device, to_torch_tensor
  from espnet.nets.pytorch_backend.rnn.argument import add_arguments_rnn_attention_common, add_arguments_rnn_decoder_common, add_arguments_rnn_encoder_common
  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 encoder_for
  from espnet.nets.scorers.ctc import CTCPrefixScorer
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 13 for calling the E2E obj's underlying member method code with example usage: obj.enhance(xs) and returns: enhanced,  mask,  ilens