class_docstr: Class that processes an example from Audio collection and returns
  a dictionary with prepared signals.
  
  For example, the output dictionary may be the following
  ```
  {
      'input_signal': input_signal_tensor,
      'target_signal': target_signal_tensor,
      'reference_signal': reference_signal_tensor,
      'embedding_vector': embedding_vector
  }
  ```
  Keys in the output dictionary are ordered with synchronous signals given first,
  followed by asynchronous signals and embedding.
  
  Args:
      sample_rate: sample rate used for all audio signals
      random_offset: If `True`, offset will be randomized when loading a subsegment
                     from a file.
class_docstr_tok: ["Class","that","processes","an","example","from","Audio","collection","and","returns","a","dictionary","with","prepared","signals",".","For","example",",","the","output","dictionary","may","be","the","following","``","`","{","'input_signal","'",":","input_signal_tensor",",","'target_signal","'",":","target_signal_tensor",",","'reference_signal","'",":","reference_signal_tensor",",","'embedding_vector","'",":","embedding_vector","}","``","`","Keys","in","the","output","dictionary","are","ordered","with","synchronous","signals","given","first",",","followed","by","asynchronous","signals","and","embedding",".","Args",":","sample_rate",":","sample","rate","used","for","all","audio","signals","random_offset",":","If","`","True","`",",","offset","will","be","randomized","when","loading","a","subsegment","from","a","file","."]
docstr: Process audio signals available in the input dictionary.
  
  Args:
      audio: A dictionary containing loaded signals `signal: tensor`
  
  Returns:
      An ordered dictionary of signals and their tensors.
code: def process_audio(
      self, audio: Dict[str, torch.Tensor]
  ) -> Dict[str, torch.Tensor]:
      """Process audio signals available in the input dictionary.
  
      Args:
          audio: A dictionary containing loaded signals `signal: tensor`
  
      Returns:
          An ordered dictionary of signals and their tensors.
      """
      # Currently, not doing any processing of the loaded signals.
      return audio
  
imports: import abc
  import math
  import random
  from collections import OrderedDict, namedtuple
  from dataclasses import dataclass
  from typing import Callable, Dict, List, Optional, Tuple, Type, Union
  import librosa
  import numpy
  import torch
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType
  from nemo.collections.common.parts.preprocessing import collections
  from nemo.collections.common.parts.utils import flatten
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import AudioSignal, EncodedRepresentation, LengthsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 14 for calling the ASRAudioProcessor obj's underlying member method code with example usage: obj.process_audio(audio) and returns: audio