code: def _create_model_output(loss, logits, base_outputs):
      if "past_key_values" in base_outputs:
          return CausalLMOutputWithPast(
              loss=loss,
              logits=logits,
              hidden_states=base_outputs.hidden_states,
              attentions=base_outputs.attentions,
              past_key_values=base_outputs.past_key_values,
          )
      else:
          return CausalLMOutput(
              loss=loss,
              logits=logits,
              hidden_states=base_outputs.hidden_states,
              attentions=base_outputs.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 5 for calling the CausalLMHead obj's underlying member method code with example usage: obj._create_model_output(loss, logits, base_outputs) and returns: CausalLMOutputWithPast, CausalLMOutput