code: def _msdd_train_collate_fn(self, batch):
      """
      Collate batch of variables that are needed for raw waveform to diarization label training.
      The following variables are included in training/validation batch:
  
      Args:
          batch (tuple):
              Batch tuple containing the variables for the diarization training.
      Returns:
          features (torch.tensor):
              Raw waveform samples (time series) loaded from the audio_filepath in the input manifest file.
          feature lengths (time series sample length):
              A list of lengths of the raw waveform samples.
          ms_seg_timestamps (torch.tensor):
              Matrix containing the start time and end time (timestamps) for each segment and each scale.
              ms_seg_timestamps is needed for extracting acoustic features from raw waveforms.
          ms_seg_counts (torch.tensor):
              Matrix containing The number of segments for each scale. ms_seg_counts is necessary for reshaping
              the input matrix for the MSDD model.
          clus_label_index (torch.tensor):
              Groundtruth Clustering label (cluster index for each segment) from RTTM files for training purpose.
              clus_label_index is necessary for calculating cluster-average embedding.
          scale_mapping (torch.tensor):
              Matrix containing the segment indices of each scale. scale_mapping is necessary for reshaping the
              multiscale embeddings to form an input matrix for the MSDD model.
          targets (torch.tensor):
              Groundtruth Speaker label for the given input embedding sequence.
      """
      packed_batch = list(zip(*batch))
      (
          features,
          feature_length,
          ms_seg_timestamps,
          ms_seg_counts,
          clus_label_index,
          scale_mapping,
          targets,
      ) = packed_batch
      features_list, feature_length_list = [], []
      (
          ms_seg_timestamps_list,
          ms_seg_counts_list,
          scale_clus_label_list,
          scale_mapping_list,
          targets_list,
      ) = (
          [],
          [],
          [],
          [],
          [],
      )
  
      max_raw_feat_len = max([x.shape[0] for x in features])
      max_target_len = max([x.shape[0] for x in targets])
      max_total_seg_len = max([x.shape[0] for x in clus_label_index])
  
      for (
          feat,
          feat_len,
          ms_seg_ts,
          ms_seg_ct,
          scale_clus,
          scl_map,
          tgt,
      ) in batch:
          seq_len = tgt.shape[0]
          pad_feat = (0, max_raw_feat_len - feat_len)
          pad_tgt = (0, 0, 0, max_target_len - seq_len)
          pad_sm = (0, max_target_len - seq_len)
          pad_ts = (0, 0, 0, max_target_len - seq_len)
          pad_sc = (0, max_total_seg_len - scale_clus.shape[0])
          padded_feat = torch.nn.functional.pad(feat, pad_feat)
          padded_tgt = torch.nn.functional.pad(tgt, pad_tgt)
          padded_sm = torch.nn.functional.pad(scl_map, pad_sm)
          padded_ms_seg_ts = torch.nn.functional.pad(ms_seg_ts, pad_ts)
          padded_scale_clus = torch.nn.functional.pad(
              scale_clus, pad_sc
          )
  
          features_list.append(padded_feat)
          feature_length_list.append(feat_len.clone().detach())
          ms_seg_timestamps_list.append(padded_ms_seg_ts)
          ms_seg_counts_list.append(ms_seg_ct.clone().detach())
          scale_clus_label_list.append(padded_scale_clus)
          scale_mapping_list.append(padded_sm)
          targets_list.append(padded_tgt)
  
      features = torch.stack(features_list)
      feature_length = torch.stack(feature_length_list)
      ms_seg_timestamps = torch.stack(ms_seg_timestamps_list)
      clus_label_index = torch.stack(scale_clus_label_list)
      ms_seg_counts = torch.stack(ms_seg_counts_list)
      scale_mapping = torch.stack(scale_mapping_list)
      targets = torch.stack(targets_list)
      return (
          features,
          feature_length,
          ms_seg_timestamps,
          ms_seg_counts,
          clus_label_index,
          scale_mapping,
          targets,
      )
  
imports: import os
  from collections import OrderedDict
  from statistics import mode
  from typing import Dict, Optional
  import torch
  from nemo.collections.asr.parts.utils.offline_clustering import get_argmin_mat
  from nemo.collections.asr.parts.utils.speaker_utils import convert_rttm_line, prepare_split_data
  from nemo.collections.common.parts.preprocessing.collections import DiarizationSpeechLabel
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import AudioSignal, EncodedRepresentation, LengthsType, NeuralType, ProbsType
label_desc: Use image node_id 4 for calling a global function with example usage: _msdd_train_collate_fn(batch) and returns: features,  feature_length,  ms_seg_timestamps,  ms_seg_counts,  clus_label_index,  scale_mapping,  targets