code: def __call__(
      self, features: List[Dict[str, Union[List[int], torch.Tensor]]]
  ) -> Dict[str, torch.Tensor]:
      # split inputs and labels since they have to be of different lenghts and need
      # different padding methods
      input_features = [
          {"input_values": feature["input_values"]}
          for feature in features
      ]
  
      batch = self.processor.pad(
          input_features,
          padding=self.padding,
          pad_to_multiple_of=self.pad_to_multiple_of,
          return_tensors="pt",
      )
  
      if self.pad_labels:
          label_features = [
              {"input_ids": feature["labels"]} for feature in features
          ]
          labels_batch = self.processor.pad(
              labels=label_features,
              padding=self.padding,
              pad_to_multiple_of=self.pad_to_multiple_of_labels,
              return_tensors="pt",
          )
  
          # replace padding with -100 to ignore loss correctly
          labels = labels_batch["input_ids"].masked_fill(
              labels_batch.attention_mask.ne(1), -100
          )
  
          # if bos token is appended in previous tokenization step,
          # cut bos token here as it's append later anyways
          if (
              self.decoder_start_token_id is not None
              and (labels[:, 0] == self.decoder_start_token_id)
              .all()
              .cpu()
              .item()
          ):
              labels = labels[:, 1:]
  
          batch["labels"] = labels
      else:
          batch["labels"] = torch.tensor(
              [feature["labels"] for feature in features]
          )
  
      return batch
  
imports: import json
  import logging
  import os
  import re
  import sys
  from collections import OrderedDict, defaultdict
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional, Union
  import datasets
  import numpy
  import torch
  from datasets import DatasetDict, load_dataset, load_metric
  import transformers
  from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForAudioClassification, AutoModelForCTC, AutoModelForSpeechSeq2Seq, AutoProcessor, AutoTokenizer, HfArgumentParser, Seq2SeqTrainer, Seq2SeqTrainingArguments, Trainer, set_seed
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  from transformers.utils import check_min_version
  from transformers.utils.versions import require_version
label_desc: Use image node_id 1 for calling the SpeechDataCollatorWithPadding obj's underlying member method code with example usage: obj.__call__(features) and returns: batch