code: def forward(
      self, input: torch.Tensor, input_lengths: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      # step 0 : get all frontends features
      self.feats = []
      for frontend in self.frontends:
          with torch.no_grad():
              input_feats, feats_lens = frontend.forward(
                  input, input_lengths
              )
          self.feats.append([input_feats, feats_lens])
  
      if (
          self.align_method == "linear_projection"
      ):  # TODO(Dan): to add other align methods
          # first step : projections
          self.feats_proj = []
          for i, frontend in enumerate(self.frontends):
              input_feats = self.feats[i][0]
              self.feats_proj.append(
                  self.projection_layers[i](input_feats)
              )
  
          # 2nd step : reshape
          self.feats_reshaped = []
          for i, frontend in enumerate(self.frontends):
              input_feats_proj = self.feats_proj[i]
              bs, nf, dim = input_feats_proj.shape
              input_feats_reshaped = torch.reshape(
                  input_feats_proj,
                  (bs, nf * self.factors[i], dim // self.factors[i]),
              )
              self.feats_reshaped.append(input_feats_reshaped)
  
          # 3rd step : drop the few last frames
          m = min([x.shape[1] for x in self.feats_reshaped])
          self.feats_final = [x[:, :m, :] for x in self.feats_reshaped]
  
          input_feats = torch.cat(
              self.feats_final, dim=-1
          )  # change the input size of the preencoder : proj_dim * n_frontends
          feats_lens = torch.ones_like(self.feats[0][1]) * (m)
  
      else:
          raise NotImplementedError
  
      return input_feats, feats_lens
  
file_path: espnet/espnet2/asr/frontend/fused.py
imports: from typing import Tuple
  import numpy
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.frontend.default import DefaultFrontend
  from espnet2.asr.frontend.s3prl import S3prlFrontend
label_desc: Use image node_id 3 for calling the FusedFrontends obj's underlying member method code with example usage: obj.forward(input, input_lengths) and returns: input_feats,  feats_lens