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.
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docstr: Return the current setup for synchronous signals.
  
  Returns:
      A dataclass containing the list of signals, their
      duration and channel selectors.
code: def sync_setup(self) -> SignalSetup:
      """Return the current setup for synchronous signals.
  
      Returns:
          A dataclass containing the list of signals, their
          duration and channel selectors.
      """
      return self._sync_setup
  
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 6 for calling the ASRAudioProcessor obj's underlying member method code with example usage: obj.sync_setup() and returns: self