code: def select_channels(
      signal: npt.NDArray,
      channel_selector: Optional[ChannelSelectorType] = None,
  ) -> npt.NDArray:
      """
      Convert a multi-channel signal to a single-channel signal by averaging over channels or selecting a single channel,
      or pass-through multi-channel signal when channel_selector is `None`.
  
      Args:
          signal: numpy array with shape (..., num_channels)
          channel selector: string denoting the downmix mode, an integer denoting the channel to be selected, or an iterable
                            of integers denoting a subset of channels. Channel selector is using zero-based indexing.
                            If set to `None`, the original signal will be returned. Uses zero-based indexing.
  
      Returns:
          numpy array
      """
      if signal.ndim == 1:
          # For one-dimensional input, return the input signal.
          if channel_selector not in [None, 0, "average"]:
              raise ValueError(
                  "Input signal is one-dimensional, channel selector (%s) cannot not be used.",
                  str(channel_selector),
              )
          return signal
  
      num_channels = signal.shape[-1]
      num_samples = (
          signal.size // num_channels
      )  # handle multi-dimensional signals
  
      if num_channels >= num_samples:
          logging.warning(
              "Number of channels (%d) is greater or equal than number of samples (%d). Check for possible transposition.",
              num_channels,
              num_samples,
          )
  
      # Samples are arranged as (num_channels, ...)
      if channel_selector is None:
          # keep the original multi-channel signal
          pass
      elif channel_selector == "average":
          # default behavior: downmix by averaging across channels
          signal = np.mean(signal, axis=-1)
      elif isinstance(channel_selector, int):
          # select a single channel
          if channel_selector >= num_channels:
              raise ValueError(
                  f"Cannot select channel {channel_selector} from a signal with {num_channels} channels."
              )
          signal = signal[..., channel_selector]
      elif isinstance(channel_selector, Iterable):
          # select multiple channels
          if max(channel_selector) >= num_channels:
              raise ValueError(
                  f"Cannot select channel subset {channel_selector} from a signal with {num_channels} channels."
              )
          signal = signal[..., channel_selector]
          # squeeze the channel dimension if a single-channel is selected
          # this is done to have the same shape as when using integer indexing
          if len(channel_selector) == 1:
              signal = np.squeeze(signal, axis=-1)
      else:
          raise ValueError(
              f"Unexpected value for channel_selector ({channel_selector})"
          )
  
      return signal
  
imports: import math
  from typing import Iterable, Optional, Union
  import librosa
  import numpy
  import numpy.typing
  import scipy
  import soundfile
  import torch
  from scipy.spatial.distance import pdist, squareform
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: select_channels(signal, channel_selector) and returns: signal, signal