code: def repeat_signal(
      signal: torch.Tensor, sig_len: int, required_length: int
  ) -> torch.Tensor:
      """repeat signal to make short signal to have required_length
      Args:
          signal (Tensor): input signal
          sig_len (int): length of input signal
          required_length (int): length of generated signal
      Returns:
          signal (Tensor): generated signal of required_length by repeating itself.
      """
      sub: torch.Tensor = torch.tensor([])
      repeat = int(required_length // sig_len)
      rem = int(required_length % sig_len)
      sub: torch.Tensor = torch.tensor([])
      rep_sig: torch.Tensor = torch.cat(repeat * [signal])
      if rem > 0:
          sub = signal[-rem:]
          signal = torch.cat((rep_sig, sub))
      else:
          signal = rep_sig
      return signal
  
imports: import io
  import os
  from typing import Dict, List, Optional, Union
  import torch
  import webdataset
  from nemo.collections.asr.data.audio_to_text import cache_datastore_manifests, expand_sharded_filepaths
  from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer
  from nemo.collections.asr.parts.preprocessing.segment import available_formats
  from nemo.collections.common.parts.preprocessing import collections
  from nemo.core.classes import Dataset, IterableDataset
  from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType, RegressionValuesType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: repeat_signal(signal, sig_len, required_length) and returns: signal