code: def generate(self, input_ids, *args, mock_response=None, **kwargs):
      assert (
          mock_response
      ), "must mock response to use DummyLLM to geenrate"
      response = self.tokenizer.encode(mock_response)[1:]
      return torch.cat(
          [input_ids, torch.tensor([response], device=self.device)],
          dim=1,
      )
  
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 3 for calling the DummyLLM obj's underlying member method code with example usage: obj.generate(input_ids) and returns: torch