class_docstr: A wrapper around an audio metric enabling selection of a specific channel
  and handling of examples in a batch with varying valid input length.
  
  Note:
      This class assumes that the underlying metric uses averaging to calculate the
      value over a batch. This assumption is only used by `forward` and does not
      impact other methods, such as `update` and `compute`.
  
  Args:
      metric: base metric that should be wrapped. It is assumed that calculation
              of the metric over a batch is done by averaging.
      channel: Optional, for selecting a channel from `preds` and `target` signals.
               If None, all channels are used.
      metric_using_batch_averaging: Optional, used to denote that the base metric
                                    is using averaging to calculate the metric value
                                    for a batch.
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docstr: Overwrite to do nothing, as in CompositionalMetric.
          
docstr_tok: ["Overwrite","to","do","nothing",",","as","in","CompositionalMetric","."]
code: def _wrap_compute(self, compute: Callable) -> Callable:
      """Overwrite to do nothing, as in CompositionalMetric."""
      return compute
  
imports: from typing import Callable, Iterable, List, Optional, Tuple
  import torch
  from torchmetrics import Metric
  from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality
  from torchmetrics.audio.pit import PermutationInvariantTraining
  from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio, SignalDistortionRatio
  from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio, SignalNoiseRatio
  from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
  from nemo.utils import logging
label_desc: Use image node_id 10 for calling the AudioMetricWrapper obj's underlying member method code with example usage: obj._wrap_compute(compute) and returns: compute