tokenizer: PreTrainedTokenizerBase
     padding: Union[bool, str, PaddingStrategy] = True
     max_length: Optional[int] = None
     pad_to_multiple_of: Optional[int] = None
     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 = sum(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",
         )
         batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
         batch["labels"] = torch.tensor(labels, dtype=torch.int64)
         return batch
</pt>
<tf>py

from dataclasses import dataclass
from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
from typing import Optional, Union
import tensorflow as tf
@dataclass
 class DataCollatorForMultipleChoice:
     """
     Data collator that will dynamically pad the inputs for multiple choice received.
     """