src/transformers/models/luke/modeling_luke.py/LukeForEntityPairClassification/forward
class LukeForEntityPairClassification:    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        entity_ids: Optional[torch.LongTensor] = None,
        entity_attention_mask: Optional[torch.FloatTensor] = None,
        entity_token_type_ids: Optional[torch.LongTensor] = None,
        entity_position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_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[Tuple, EntityPairClassificationOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
            Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
            used for the single-label classification. In this case, labels should contain the indices that should be in
            `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
            loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
            and 1 indicate false and true, respectively.

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, LukeForEntityPairClassification

        >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
        >>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")

        >>> text = "Beyoncé lives in Los Angeles."
        >>> entity_spans = [
        ...     (0, 7),
        ...     (17, 28),
        ... ]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
        >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> predicted_class_idx = logits.argmax(-1).item()
        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
        Predicted class: per:cities_of_residence
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.luke(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            entity_ids=entity_ids,
            entity_attention_mask=entity_attention_mask,
            entity_token_type_ids=entity_token_type_ids,
            entity_position_ids=entity_position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=True,
        )

        feature_vector = torch.cat(
            [outputs.entity_last_hidden_state[:, 0, :], outputs.entity_last_hidden_state[:, 1, :]], dim=1
        )
        feature_vector = self.dropout(feature_vector)
        logits = self.classifier(feature_vector)

        loss = None
        if labels is not None:
            # When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
            # cross entropy is used otherwise.
            if labels.ndim == 1:
                loss = nn.functional.cross_entropy(logits, labels)
            else:
                loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))

        if not return_dict:
            return tuple(
                v
                for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
                if v is not None
            )

        return EntityPairClassificationOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            entity_hidden_states=outputs.entity_hidden_states,
            attentions=outputs.attentions,
        )