code: def load_neuron_explanations(
      explanations_path: str,
      layer_index: Union[str, int],
      neuron_index: Union[str, int],
  ) -> Optional[NeuronSimulationResults]:
      """Load scored explanations for the specified neuron."""
      file = bf.join(
          explanations_path, str(layer_index), f"{neuron_index}.jsonl"
      )
      if not bf.exists(file):
          return None
      with bf.BlobFile(file) as f:
          for line in f:
              return loads(line)
      return None
  
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 a global function with example usage: load_neuron_explanations(explanations_path, layer_index, neuron_index) and returns: None, None, loads