class_docstr: Data collator that will dynamically pad the inputs for multiple choice received.
  
  Args:
      tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
          The tokenizer used for encoding the data.
      padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
          Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
          among:
  
          - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
            if provided).
          - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
            acceptable input length for the model if that argument is not provided.
          - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
            lengths).
      max_length (`int`, *optional*):
          Maximum length of the returned list and optionally padding length (see above).
      pad_to_multiple_of (`int`, *optional*):
          If set will pad the sequence to a multiple of the provided value.
  
          This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
          7.5 (Volta).
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code: def __call__(self, features):
      label_name = (
          "label" if "label" in features[0].keys() else "labels"
      )
      labels = [feature.pop(label_name) for feature in features]
      batch_size = len(features)
      num_choices = len(features[0]["input_ids"])
      flattened_features = [
          [
              {k: v[i] for k, v in feature.items()}
              for i in range(num_choices)
          ]
          for feature in features
      ]
      flattened_features = list(chain(*flattened_features))
  
      batch = self.tokenizer.pad(
          flattened_features,
          padding=self.padding,
          max_length=self.max_length,
          pad_to_multiple_of=self.pad_to_multiple_of,
          return_tensors="pt",
      )
  
      # Un-flatten
      batch = {
          k: v.view(batch_size, num_choices, -1)
          for k, v in batch.items()
      }
      # Add back labels
      batch["labels"] = torch.tensor(labels, dtype=torch.int64)
      return batch
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from itertools import chain
  from typing import Optional, Union
  import datasets
  import numpy
  import torch
  from datasets import load_dataset
  import adapters
  import transformers
  from adapters import AdapterArguments, AdapterTrainer, setup_adapter_training
  from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import PaddingStrategy, check_min_version
label_desc: Use image node_id 1 for calling the DataCollatorForMultipleChoice obj's underlying member method code with example usage: obj.__call__(features) and returns: batch