code: def _audio_collate_fn(batch: List[dict]) -> Tuple[torch.Tensor]:
      """Collate a batch of items returned by __getitem__.
      Examples for each signal are zero padded to the same length
      (batch_length), which is determined by the longest example.
      Lengths of the original signals are returned in the output.
  
      Args:
          batch: List of dictionaries. Each element of the list
              has the following format
              ```
              {
                  'signal_0': 1D or 2D tensor,
                  'signal_1': 1D or 2D tensor,
                  ...
                  'signal_N': 1D or 2D tensor,
              }
              ```
              1D tensors have shape (num_samples,) and 2D tensors
              have shape (num_channels, num_samples)
  
      Returns:
          A tuple containing signal tensor and signal length tensor (in samples)
          for each signal.
          The output has the following format:
          ```
          (signal_0, signal_0_length, signal_1, signal_1_length, ..., signal_N, signal_N_length)
          ```
          Note that the output format is obtained by interleaving signals and their length.
      """
      signals = batch[0].keys()
  
      batched = tuple()
  
      for signal in signals:
          signal_length = [b[signal].shape[-1] for b in batch]
          # Batch length is determined by the longest signal in the batch
          batch_length = max(signal_length)
          b_signal = []
          for s_len, b in zip(signal_length, batch):
              # check if padding is necessary
              if s_len < batch_length:
                  if b[signal].ndim == 1:
                      # single-channel signal
                      pad = (0, batch_length - s_len)
                  elif b[signal].ndim == 2:
                      # multi-channel signal
                      pad = (0, batch_length - s_len, 0, 0)
                  else:
                      raise RuntimeError(
                          f"Signal {signal} has unsuported dimensions {signal.shape}. Currently, only 1D and 2D arrays are supported."
                      )
                  b[signal] = torch.nn.functional.pad(b[signal], pad)
              # append the current padded signal
              b_signal.append(b[signal])
          # (signal_batched, signal_length)
          batched += (
              torch.stack(b_signal),
              torch.tensor(signal_length, dtype=torch.int32),
          )
  
      # Currently, outputs are expected to be in a tuple, where each element must correspond
      # to the output type in the OrderedDict returned by output_types.
      #
      # Therefore, we return batched signals by interleaving signals and their length:
      #   (signal_0, signal_0_length, signal_1, signal_1_length, ...)
      return batched
  
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 1 for calling a global function with example usage: _audio_collate_fn(batch) and returns: batched