class_docstr: Simulator parameters and the results of scoring it on multiple sequences
class_docstr_tok: ["Simulator","parameters","and","the","results","of","scoring","it","on","multiple","sequences"]
docstr: This method may return None in cases where the score is undefined, for example if the
  normalized activations were all zero, yielding a correlation coefficient of NaN.
docstr_tok: ["This","method","may","return","None","in","cases","where","the","score","is","undefined",",","for","example","if","the","normalized","activations","were","all","zero",",","yielding","a","correlation","coefficient","of","NaN","."]
code: def get_preferred_score(self) -> Optional[float]:
      """
      This method may return None in cases where the score is undefined, for example if the
      normalized activations were all zero, yielding a correlation coefficient of NaN.
      """
      return self.scored_simulation.get_preferred_score()
  
imports: from __future__ import annotations
  import json
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  import blobfile
  import boostedblob
  from neuron_explainer.activations.activations import NeuronId
  from neuron_explainer.fast_dataclasses import FastDataclass, loads, register_dataclass
label_desc: Use image node_id 1 for calling the ScoredExplanation obj's underlying member method code with example usage: obj.get_preferred_score() and returns: self