vision for
            aggregation (WikiSQL-supervised).
        float_answer (`torch.FloatTensor` of shape `(batch_size, )`, *optional*):
            Float answer for every example in the batch. Set to *float('nan')* for cell selection questions. Only
            required in case of weak supervision (WTQ) to calculate the aggregate mask and regression loss.
        numeric_values (`torch.FloatTensor` of shape `(batch_size, seq_length)`, *optional*):
            Numeric values of every token, NaN for tokens which are not numeric values. Can be obtained using
            [`AutoTokenizer`]. Only required in case of weak supervision for aggregation (WTQ) to calculate the
            regression loss.
        numeric_values_scale (`torch.FloatTensor` of shape `(batch_size, seq_length)`, *optional*):
            Scale of the numeric values of every token. Can be obtained using [`AutoTokenizer`]. Only required in case
            of weak supervision for aggregation (WTQ) to calculate the regression loss.

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TapasForQuestionAnswering
        >>> import pandas as pd

        >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-wtq")
        >>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

        >>> data = {
        ...     "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
        ...     "Age": ["56", "45", "59"],
        ...     "Number of movies": ["87", "53", "69"],
        ... }
        >>> table = pd.DataFrame.from_dict(data)
        >>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"]

        >>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> logits = outputs.logits
        >>> logits_aggregation = outputs.logits_aggregation
        ```N©r¼