code: def forward(
      self,
      outputs,
      cls_output=None,
      attention_mask=None,
      return_dict=False,
      **kwargs,
  ):
      # First, pass through all layers except the last embedding layer
      seq_outputs = outputs[0]
      for i in range(len(self) - 1):
          seq_outputs = self[i](seq_outputs)
  
      # Now, pass through an invertible adapter if available
      inv_adapter = kwargs.pop("invertible_adapter", None)
      if inv_adapter is not None:
          seq_outputs = inv_adapter(seq_outputs, rev=True)
  
      # Finally, pass through the last embedding layer
      lm_logits = self[len(self) - 1](seq_outputs)
  
      loss = None
      labels = kwargs.pop("labels", None)
      if labels is not None:
          loss_fct = nn.CrossEntropyLoss()
          if self.config["shift_labels"]:
              logits_for_loss = lm_logits[..., :-1, :].contiguous()
              labels = labels[..., 1:].contiguous()
          else:
              logits_for_loss = lm_logits
  
          # adjust labels for prompt tuning
          if kwargs.get("prompt_tokens_length", 0) > 0:
              prompt_length = kwargs.get("prompt_tokens_length")
              prompt_labels = torch.full(
                  (labels.shape[0], prompt_length),
                  loss_fct.ignore_index,
                  dtype=torch.long,
                  device=labels.device,
              )
              labels = torch.cat((prompt_labels, labels), dim=-1)
  
          loss = loss_fct(
              logits_for_loss.view(-1, self.config["vocab_size"]),
              labels.view(-1),
          )
  
      if return_dict:
          return self._create_model_output(loss, lm_logits, outputs)
      else:
          outputs = (lm_logits,) + outputs[1:]
          if loss is not None:
              outputs = (loss,) + outputs
          return outputs
  
imports: import torch
  import torch.nn
  from transformers.modeling_outputs import CausalLMOutput, CausalLMOutputWithPast, MaskedLMOutput, Seq2SeqLMOutput
  from ..methods.modeling import Activation_Function_Class
  from .base import PredictionHead
label_desc: Use image node_id 6 for calling the CausalLMHead obj's underlying member method code with example usage: obj.forward(outputs, cls_output, attention_mask, return_dict) and returns: self, outputs