code: def prepare_inputs_for_generation(self, model_inp, **model_kwargs):
      if "past_key_values" in model_kwargs:
          emb_len = model_kwargs["past_key_values"].shape[1]
          return {
              "input_ids": model_inp[:, emb_len:],
              "past_key_values": model_kwargs["past_key_values"],
          }
      return {"input_ids": model_inp}
  
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 5 for calling the DummyLLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(model_inp) and returns: dict, dict