code: def slice_segments(x, ids_str, segment_size=4):
      """
      Time-wise slicing (patching) of bathches for audio/spectrogram
      [B x C x T] -> [B x C x segment_size]
      """
      ret = torch.zeros_like(x[:, :, :segment_size])
      for i in range(x.size(0)):
          idx_str = ids_str[i]
          idx_end = idx_str + segment_size
          x_i = x[i]
          if idx_end >= x.size(2):
              # pad the sample if it is shorter than the segment size
              x_i = torch.nn.functional.pad(
                  x_i, (0, (idx_end + 1) - x.size(2))
              )
          ret[i] = x_i[:, idx_str:idx_end]
      return ret
  
imports: from enum import Enum
  from typing import Optional, Tuple
  import librosa
  import matplotlib.pylab
  import numpy
  import torch
  from einops import rearrange
  from numba import jit, prange
  from nemo.collections.tts.torch.tts_data_types import DATA_STR2DATA_CLASS, MAIN_DATA_TYPES, WithLens
  from nemo.utils import logging
  from nemo.utils.decorators import deprecated
label_desc: Use image node_id 26 for calling a global function with example usage: slice_segments(x, ids_str, segment_size) and returns: ret