docstr: Ensures all audio samples have a specific number of channels.
  
  To reduce the number of audio channels from 2 to 1, the average values of the
  two channels is used.
  
  Args:
      data (Dict): {
          "samples": {
              "audio": Tensor[N,T,C] where N is the number of audio clips, T is
                       the audio sequence length, and C is the number of channels.
          }
      }
  
  
  Returns:
      Dict: _description_
docstr_tok: ["Ensures","all","audio","samples","have","a","specific","number","of","channels",".","To","reduce","the","number","of","audio","channels","from","2","to","1",",","the","average","values","of","the","two","channels","is","used",".","Args",":","data","(","Dict",")",":","{","``","samples","''",":","{","``","audio","''",":","Tensor","[","N",",","T",",","C","]","where","N","is","the","number","of","audio","clips",",","T","is","the","audio","sequence","length",",","and","C","is","the","number","of","channels.","}","}","Returns",":","Dict",":","_description_"]
code: def __call__(self, data: Dict, *args, **kwargs) -> Dict:
      """Ensures all audio samples have a specific number of channels.
  
      To reduce the number of audio channels from 2 to 1, the average values of the
      two channels is used.
  
      Args:
          data (Dict): {
              "samples": {
                  "audio": Tensor[N,T,C] where N is the number of audio clips, T is
                           the audio sequence length, and C is the number of channels.
              }
          }
  
  
      Returns:
          Dict: _description_
      """
      if not self.enable:
          return data
  
      audio = data["samples"]["audio"]  # N, T, C
      assert (
          audio.ndim == 3
      ), f"Invalid audio dimension {audio.ndim}. Expected 3."
      num_input_channels = audio.shape[2]
  
      if num_input_channels == self.num_channels:
          return data
  
      if (num_input_channels, self.num_channels) == (1, 2):
          audio = audio.repeat(1, 1, 2)  # N, T, 2
      elif (num_input_channels, self.num_channels) == (2, 1):
          audio = audio.mean(dim=2, keepdim=True)  # N, T, 1
      else:
          raise NotImplementedError(
              f"The logic for standardizing audio channels with input shape of"
              f" {audio.shape} to {self.num_channels} channels is not implemented."
          )
  
      data["samples"]["audio"] = 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 StandardizeChannels obj's underlying member method code with example usage: obj.__call__(data) and returns: data, data, data