code: def splice_frames(x, frame_splicing):
      """Stacks frames together across feature dim
  
      input is batch_size, feature_dim, num_frames
      output is batch_size, feature_dim*frame_splicing, num_frames
  
      """
      seq = [x]
      for n in range(1, frame_splicing):
          seq.append(torch.cat([x[:, :, :n], x[:, :, n:]], dim=2))
      return torch.cat(seq, dim=1)
  
imports: import math
  import random
  from typing import Optional, Tuple, Union
  import librosa
  import numpy
  import torch
  import torch.nn
  from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling a global function with example usage: splice_frames(x, frame_splicing) and returns: torch