code: def stack_subsample_frames(x, stacking=1, subsampling=1):
      """Stacks frames together across feature dim, and then subsamples
  
      input is batch_size, feature_dim, num_frames
      output is batch_size, feature_dim * stacking, num_frames / subsampling
  
      """
      seq = [x]
      for n in range(1, stacking):
          tmp = torch.zeros_like(x)
          tmp[:, :, :-n] = x[:, :, n:]
          seq.append(tmp)
      x = torch.cat(seq, dim=1)[:, :, ::subsampling]
      return x
  
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 2 for calling a global function with example usage: stack_subsample_frames(x, stacking, subsampling) and returns: x