class_docstr: This class implements gain augmentation for audio.
docstr: This function implements the gain transformation by scaling the
  input audio with a specific constant determined from the gain_levels
  
  Args:
      data: A dictionary containing {"samples": {"audio": @audio}}, where
          @audio is a tensor of shape [num_channels, sequence_length].
  
  Returns:
      The modified dictionary with the augmented audio.
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code: def __call__(self, data: Dict) -> Dict:
      """
      This function implements the gain transformation by scaling the
      input audio with a specific constant determined from the gain_levels
  
      Args:
          data: A dictionary containing {"samples": {"audio": @audio}}, where
              @audio is a tensor of shape [num_channels, sequence_length].
  
      Returns:
          The modified dictionary with the augmented audio.
      """
      audio = data["samples"]["audio"]
      gain_level = random.choice(self.gain_levels)
      augmented_audio = 10.0 ** (gain_level / 20.0) * audio
      data["samples"]["audio"] = augmented_audio
      return data
  
imports: import argparse
  import math
  import pathlib
  import random
  from typing import Callable, Dict, List, Optional, Union
  import torch
  import torchaudio
  from data.transforms import TRANSFORMATIONS_REGISTRY, BaseTransformation
  from data.transforms.audio_aux import mfccs
label_desc: Use image node_id 3 for calling the Gain obj's underlying member method code with example usage: obj.__call__(data) and returns: data