code: def forward(
      self,
      input_ids,
      token_type_ids=None,
      attention_mask=None,
      labels=None,
      position_ids=None,
      head_mask=None,
  ):
      outputs = self.roberta(
          input_ids,
          position_ids=position_ids,
          token_type_ids=token_type_ids,
          attention_mask=attention_mask,
          head_mask=head_mask,
      )
      sequence_output = outputs[0]
      logits = self.classifier(sequence_output)
  
      outputs = (logits,) + outputs[2:]
  
      if labels is not None:
          loss_fct = BCEWithLogitsLoss(
              weight=self.weight, pos_weight=self.pos_weight
          )
  
          loss = loss_fct(
              logits.view(-1, self.num_labels),
              labels.view(-1, self.num_labels),
          )
          outputs = (loss,) + outputs
  
      return outputs
  
imports: from transformers import BertForSequenceClassification, BertModel, BertConfig, XLNetForSequenceClassification, RobertaModel, RobertaConfig, BertPreTrainedModel, RobertaForSequenceClassification, DistilBertForSequenceClassification, CamembertForSequenceClassification, AlbertForSequenceClassification, ElectraForSequenceClassification, XLMModel, XLMConfig, XLMTokenizer, XLMForSequenceClassification, FlaubertModel, FlaubertConfig, FlaubertTokenizer, FlaubertForSequenceClassification
  import torch
  from torch import Tensor
  from torch.nn import BCEWithLogitsLoss
label_desc: Use image node_id 1 for calling the CamembertForMultiLabelSequenceClassification obj's underlying member method code with example usage: obj.forward(input_ids, token_type_ids, attention_mask, labels, position_ids, head_mask) and returns: outputs