code: def pad_sample_indices(
      start_sample: int,
      end_sample: int,
      max_sample: int,
      sample_rate: int,
      pad_seconds: float,
  ) -> Tuple[int, int]:
      """Shift the input sample indices by pad_seconds in front and back within [0, max_sample]
      Args:
          start_sample: Start sample index
          end_sample: End sample index
          max_sample: Maximum sample index
          sample_rate: Sample rate of audio
          pad_seconds: Amount to pad/shift the indices by.
  
      Returns the sample indices after padding by the input amount.
      """
      pad_samples = int(pad_seconds * sample_rate)
      start_sample = start_sample - pad_samples
      end_sample = end_sample + pad_samples
  
      start_sample = max(0, start_sample)
      end_sample = min(max_sample, end_sample)
  
      return start_sample, end_sample
  
imports: from abc import ABC, abstractmethod
  from typing import Tuple
  import librosa
  import numpy
  import torch
  from nemo.collections.asr.models import EncDecClassificationModel
  from nemo.collections.tts.parts.utils.tts_dataset_utils import normalize_volume
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: pad_sample_indices(start_sample, end_sample, max_sample, sample_rate, pad_seconds) and returns: start_sample,  end_sample