code: def forward(self, inp, seq_len):
      x = inp
      dtype = x.dtype
  
      if self.pad_amount > 0:
          x = torch.nn.functional.pad(
              x.unsqueeze(1),
              (self.pad_amount, self.pad_amount),
              "reflect",
          ).squeeze(1)
          seq_len = seq_len + 2 * self.pad_amount
  
      seq_len = self.get_seq_len(seq_len)
  
      # do preemphasis
      if self.preemph is not None:
          x = torch.cat(
              (
                  x[:, 0].unsqueeze(1),
                  x[:, 1:] - self.preemph * x[:, :-1],
              ),
              dim=1,
          )
  
      # do stft
      x = torch.stft(
          x,
          n_fft=self.n_fft,
          hop_length=self.hop_length,
          win_length=self.win_length,
          pad_mode="reflect",
          center=True,
          window=self.window.to(dtype=torch.float).to(x.device),
          return_complex=True,
      )
      x = torch.view_as_real(x)
  
      # get power spectrum
      x = x.pow(2).sum(-1)
  
      # dot with filterbank energies
      x = torch.matmul(self.fb.to(x.dtype), x)
  
      # log features if required
      if self.log:
          x = torch.log(x + 1e-20)
  
      # frame splicing if required
      if self.frame_splicing_stack > 1 or self.frame_splicing_subsample:
          x = stack_subsample_frames(
              x,
              stacking=self.frame_splicing_stack,
              subsampling=self.frame_splicing_subsample,
          )
  
      # normalize if required
      if self.normalize:
          x = self.normalize_batch(
              x, seq_len, normalize_type=self.normalize
          )
  
      # mask to zero any values beyond seq_len in batch,
      # pad to multiple of `pad_to` (for efficiency)
      max_len = x.size(-1)
      seq = torch.arange(max_len).to(seq_len.dtype).to(x.device)
      mask = seq.expand(x.size(0), max_len) >= seq_len.unsqueeze(1)
      x = x.masked_fill(mask.unsqueeze(1).to(device=x.device), 0)
      return x.to(dtype)
  
imports: import nose_utils
  import nvidia.dali.fn
  from nvidia.dali import pipeline_def
  import nvidia.dali.types
  from test_utils import get_files, to_array
  import numpy
  import librosa
  import torch
  import math
  import random
  import os
  from nose.tools import nottest
label_desc: Use image node_id 4 for calling the FilterbankFeatures obj's underlying member method code with example usage: obj.forward(inp, seq_len) and returns: x