code: def test_llm_attr_fa_log_prob(self) -> None:
      llm = DummyLLM()
      llm.to(self.device)
      tokenizer = DummyTokenizer()
      fa = FeatureAblation(llm)
      llm_fa = LLMAttribution(fa, tokenizer, attr_target="log_prob")
  
      inp = TextTemplateInput("{} b {} {} e {}", ["a", "c", "d", "f"])
      res = llm_fa.attribute(
          inp, "m n o p q", use_cached_outputs=self.use_cached_outputs
      )
  
      # With FeatureAblation, the seq attr in log_prob
      # equals to the sum of each token attr
      assertTensorAlmostEqual(
          self, res.seq_attr, cast(Tensor, res.token_attr).sum(0)
      )
  
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 TestLLMAttr obj's underlying member method code with example usage: obj.test_llm_attr_fa_log_prob() without return types