code: def _create_model_output(loss, logits, base_outputs):
      return Seq2SeqLMOutput(
          loss=loss,
          logits=logits,
          past_key_values=base_outputs.past_key_values,
          decoder_hidden_states=base_outputs.decoder_hidden_states,
          decoder_attentions=base_outputs.decoder_attentions,
          cross_attentions=base_outputs.cross_attentions,
          encoder_last_hidden_state=base_outputs.encoder_last_hidden_state,
          encoder_hidden_states=base_outputs.encoder_hidden_states,
          encoder_attentions=base_outputs.encoder_attentions,
      )
  
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 2 for calling the Seq2SeqLMHead obj's underlying member method code with example usage: obj._create_model_output(loss, logits, base_outputs) and returns: Seq2SeqLMOutput