src/transformers/models/layoutlm/modeling_layoutlm.py/LayoutLMForSequenceClassification/forward
class LayoutLMForSequenceClassification:    def forward(
        self,
        input_ids=None,
        bbox=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples::

            >>> from transformers import LayoutLMTokenizer, LayoutLMForSequenceClassification
            >>> import torch

            >>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
            >>> model = LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased')

            >>> words = ["Hello", "world"]
            >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

            >>> token_boxes = []
            >>> for word, box in zip(words, normalized_word_boxes):
            ...     word_tokens = tokenizer.tokenize(word)
            ...     token_boxes.extend([box] * len(word_tokens))
            >>> # add bounding boxes of cls + sep tokens
            >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

            >>> encoding = tokenizer(' '.join(words), return_tensors="pt")
            >>> input_ids = encoding["input_ids"]
            >>> attention_mask = encoding["attention_mask"]
            >>> token_type_ids = encoding["token_type_ids"]
            >>> bbox = torch.tensor([token_boxes])
            >>> sequence_label = torch.tensor([1])

            >>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
            ...                 labels=sequence_label)

            >>> loss = outputs.loss
            >>> logits = outputs.logits
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.layoutlm(
            input_ids=input_ids,
            bbox=bbox,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1), labels.view(-1))
            else:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        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,
        )