src/transformers/models/vilt/modeling_vilt.py/ViltForQuestionAnswering/forward
class ViltForQuestionAnswering:    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        pixel_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        image_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
        r"""
        labels (`torch.FloatTensor` of shape `(batch_size, num_labels)`, *optional*):
            Labels for computing the visual question answering loss. This tensor must be either a one-hot encoding of
            all answers that are applicable for a given example in the batch, or a soft encoding indicating which
            answers are applicable, where 1.0 is the highest score.

        Returns:

        Examples:

        ```python
        >>> from transformers import ViltProcessor, ViltForQuestionAnswering
        >>> import requests
        >>> from PIL import Image

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> text = "How many cats are there?"

        >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
        >>> model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

        >>> # prepare inputs
        >>> encoding = processor(image, text, return_tensors="pt")

        >>> # forward pass
        >>> outputs = model(**encoding)
        >>> logits = outputs.logits
        >>> idx = logits.argmax(-1).item()
        >>> print("Predicted answer:", model.config.id2label[idx])
        Predicted answer: 2
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.vilt(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            pixel_values=pixel_values,
            pixel_mask=pixel_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            image_embeds=image_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooler_output = outputs.pooler_output if return_dict else outputs[1]

        logits = self.classifier(pooler_output)

        loss = None
        if labels is not None:
            loss = nn.functional.binary_cross_entropy_with_logits(logits, labels) * labels.shape[1]
            # see https://github.com/jnhwkim/ban-vqa/blob/master/train.py#L19

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )