code: def _audio_feature_collate_fn(batch, feat_pad_val, label_pad_id):
      """collate batch of audio feature, audio len, labels, labels len
      Args:
          batch (Optional[FloatTensor], Optional[LongTensor], LongTensor,
                 LongTensor):  A tuple of tuples of feature, feature lengths,
                 labels, and label lengths.  This collate func assumes the
                 features are torch tensors of Log-Melspectrogram (i.e. [N_MEL, T]).
      """
      packed_batch = list(zip(*batch))
      if len(packed_batch) == 5:
          _, feat_lengths, _, labels_lengths, sample_ids = packed_batch
      elif len(packed_batch) == 4:
          sample_ids = None
          _, feat_lengths, _, labels_lengths = packed_batch
      else:
          raise ValueError("Expects 4 or 5 tensors in the batch!")
      max_feat_len = 0
      has_feat = feat_lengths[0] is not None
      if has_feat:
          max_feat_len = max(feat_lengths).item()
      max_labels_len = max(labels_lengths).item()
  
      features, labels = [], []
      for b in batch:
          feat_i, feat_i_len, label_i, label_i_len = (
              b[0],
              b[1],
              b[2],
              b[3],
          )
  
          if has_feat:
              feat_i_len = feat_i_len.item()
              if feat_i_len < max_feat_len:
                  pad = (0, max_feat_len - feat_i_len)
                  feat_i = torch.nn.functional.pad(
                      feat_i, pad, value=feat_pad_val
                  )
              features.append(feat_i)
  
          label_i_len = label_i_len.item()
          if label_i_len < max_labels_len:
              pad = (0, max_labels_len - label_i_len)
              label_i = torch.nn.functional.pad(
                  label_i, pad, value=label_pad_id
              )
          labels.append(label_i)
  
      if has_feat:
          features = torch.stack(features)
          feature_lengths = torch.stack(feat_lengths)
      else:
          features, feat_lengths = None, None
      labels = torch.stack(labels)
      labels_lengths = torch.stack(labels_lengths)
  
      if sample_ids is None:
          return features, feature_lengths, labels, labels_lengths
      else:
          sample_ids = torch.tensor(sample_ids, dtype=torch.int32)
          return (
              features,
              feature_lengths,
              labels,
              labels_lengths,
              sample_ids,
          )
  
imports: from typing import Dict, List, Optional
  import torch
  from nemo.collections.asr.parts.preprocessing.feature_loader import ExternalFeatureLoader
  from nemo.collections.common.parts.preprocessing import collections
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import AcousticEncodedRepresentation, LabelsType, LengthsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: _audio_feature_collate_fn(batch, feat_pad_val, label_pad_id) and returns: features,  feature_lengths,  labels,  labels_lengths, features,  feature_lengths,  labels,  labels_lengths,  sample_ids