src/transformers/models/layoutlm/modeling_layoutlm.py/LayoutLMForTokenClassification/forward
class LayoutLMForTokenClassification:    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, sequence_length)`, `optional`):
            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
            1]``.

        Returns:

        Examples::

            >>> from transformers import LayoutLMTokenizer, LayoutLMForTokenClassification
            >>> import torch

            >>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
            >>> model = LayoutLMForTokenClassification.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])
            >>> token_labels = torch.tensor([1,1,0,0]).unsqueeze(0) # batch size of 1

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

            >>> 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,
        )

        sequence_output = outputs[0]

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

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()

            if attention_mask is not None:
                active_loss = attention_mask.view(-1) == 1
                active_logits = logits.view(-1, self.num_labels)
                active_labels = torch.where(
                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
                )
                loss = loss_fct(active_logits, active_labels)
            else:
                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 TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )