code: def __init__(self):
      super().__init__()
      self.tokenizer = DummyTokenizer()
      self.emb = nn.Embedding(self.tokenizer.vocab_size, 10)
      self.linear = nn.Linear(10, self.tokenizer.vocab_size)
      self.trans = nn.TransformerEncoderLayer(d_model=10, nhead=2)
  
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 1 to create a new DummyLLM object from inherited base classes: nn with example: obj = DummyLLM()