text: import torch.nn as nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.models.bert.modeling_bert import BertPreTrainedModel
  from transformers.models.layoutlm.modeling_layoutlm import LayoutLMModel
  
  
  class LayoutLMForSequenceClassification(BertPreTrainedModel):
      def __init__(self, config):
          super(LayoutLMForSequenceClassification, self).__init__(config)
          self.num_labels = config.num_labels
  
          self.bert = LayoutLMModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
  
          self.init_weights()
  
      def forward(
          self,
          input_ids,
          bbox,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
      ):
  
          outputs = self.bert(
              input_ids=input_ids,
              bbox=bbox,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
          )
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
  
          outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
  
          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:
                  if self.weight is not None:
                      weight = self.weight.to(labels.device)
                  else:
                      weight = None
                  loss_fct = CrossEntropyLoss(weight=weight)
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              outputs = (loss,) + outputs
  
          return outputs  # (loss), logits, (hidden_states), (attentions)
  