class_docstr: Data class for the return result of LLMAttribution,
  which includes the necessary properties of the attribution.
  It also provides utilities to help present and plot the result in different forms.
class_docstr_tok: ["Data","class","for","the","return","result","of","LLMAttribution",",","which","includes","the","necessary","properties","of","the","attribution",".","It","also","provides","utilities","to","help","present","and","plot","the","result","in","different","forms","."]
code: def __init__(
      self,
      seq_attr: Tensor,
      token_attr: Union[Tensor, None],
      input_tokens: List[str],
      output_tokens: List[str],
  ):
      self.seq_attr = seq_attr
      self.token_attr = token_attr
      self.input_tokens = input_tokens
      self.output_tokens = output_tokens
  
imports: from copy import copy
  from typing import Callable, cast, Dict, List, Optional, Union
  import matplotlib.pyplot
  import numpy
  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.shapley_value import ShapleyValues, ShapleyValueSampling
  from captum.attr._utils.attribution import Attribution
  from captum.attr._utils.interpretable_input import InterpretableInput, TextTemplateInput, TextTokenInput
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new LLMAttributionResult object  with example: obj = LLMAttributionResult(seq_attr, token_attr, input_tokens, output_tokens)