code: def forward(
      self,
      input_ids=None,
      attention_mask=None,
      langs=None,
      token_type_ids=None,
      position_ids=None,
      lengths=None,
      cache=None,
      head_mask=None,
      inputs_embeds=None,
      labels=None,
      output_attentions=None,
      output_hidden_states=None,
  ):
      transformer_outputs = self.transformer(
          input_ids,
          attention_mask=attention_mask,
          langs=langs,
          token_type_ids=token_type_ids,
          position_ids=position_ids,
          lengths=lengths,
          cache=cache,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
      )
  
      output = transformer_outputs[0]
      logits = self.sequence_summary(output)
  
      outputs = (logits,) + transformer_outputs[
          1:
      ]  # Keep new_mems and attention/hidden states if they are here
  
      # Keep mems, hidden states, attentions if there are in it
      outputs = (logits,) + transformer_outputs[1:]
  
      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 FlaubertForMultiLabelSequenceClassification obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, langs, token_type_ids, position_ids, lengths, cache, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states) and returns: outputs