...             str
...         )  # be sure to make your table data text only
...         encoding = self.tokenizer(
...             table=table,
...             queries=item.question,
...             answer_coordinates=item.answer_coordinates,
...             answer_text=item.answer_text,
...             truncation=True,
...             padding="max_length",
...             return_tensors="pt",
...         )
...         # remove the batch dimension which the tokenizer adds by default
...         encoding = {key: val.squeeze(0) for key, val in encoding.items()}
...         # add the float_answer which is also required (weak supervision for aggregation case)
...         encoding["float_answer"] = torch.tensor(item.float_answer)
...         return encoding
...     def len(self):
...         return len(self.data)
