code: def forward(self, input_ids, *args, **kwargs):
      emb = self.emb(input_ids)
      if "past_key_values" in kwargs:
          emb = torch.cat((kwargs["past_key_values"], emb), dim=1)
      logits = self.linear(self.trans(emb))
      Result = namedtuple("Result", ["logits", "past_key_values"])
      return Result(logits=logits, past_key_values=emb)
  
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 2 for calling the DummyLLM obj's underlying member method code with example usage: obj.forward(input_ids) and returns: Result