code: def _update_model_kwargs_for_generation(self, outputs, model_kwargs):
      new_kwargs = copy.deepcopy(model_kwargs)
      if hasattr(outputs, "past_key_values"):
          new_kwargs["past_key_values"] = outputs.past_key_values
      return new_kwargs
  
imports: import copy
  from collections import namedtuple
  from typing import cast, List, Optional, Union
  import torch
  from captum.attr._core.feature_ablation import FeatureAblation
  from captum.attr._core.kernel_shap import KernelShap
  from captum.attr._core.layer.layer_integrated_gradients import LayerIntegratedGradients
  from captum.attr._core.lime import Lime
  from captum.attr._core.llm_attr import LLMAttribution, LLMGradientAttribution
  from captum.attr._core.shapley_value import ShapleyValueSampling
  from captum.attr._utils.interpretable_input import TextTemplateInput, TextTokenInput
  from parameterized import parameterized, parameterized_class
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from torch import nn, Tensor
label_desc: Use image node_id 4 for calling the DummyLLM obj's underlying member method code with example usage: obj._update_model_kwargs_for_generation(outputs, model_kwargs) and returns: new_kwargs