class_docstr: Abstract base class for Explainer classes that generate explanations from subclass-specific
  input data.
code: def __init__(
      self,
      model_name: str,
      prompt_format: PromptFormat = PromptFormat.HARMONY_V4,
      # This parameter lets us adjust the length of the prompt when we're generating explanations
      # using older models with shorter context windows. In the future we can use it to experiment
      # with longer context windows.
      context_size: ContextSize = ContextSize.FOUR_K,
      max_concurrent: Optional[int] = 10,
      cache: bool = False,
  ):
      if prompt_format == PromptFormat.HARMONY_V4:
          assert model_name in HARMONY_V4_MODELS
      elif prompt_format in [
          PromptFormat.NONE,
          PromptFormat.INSTRUCTION_FOLLOWING,
      ]:
          assert model_name not in HARMONY_V4_MODELS
      else:
          raise ValueError(f"Unhandled prompt format {prompt_format}")
  
      self.model_name = model_name
      self.prompt_format = prompt_format
      self.context_size = context_size
      self.client = ApiClient(
          model_name=model_name,
          max_concurrent=max_concurrent,
          cache=cache,
      )
  
imports: from __future__ import annotations
  import logging
  import re
  from abc import ABC, abstractmethod
  from enum import Enum
  from typing import Any, Optional, Sequence, Union
  from neuron_explainer.activations.activation_records import calculate_max_activation, format_activation_records, non_zero_activation_proportion
  from neuron_explainer.activations.activations import ActivationRecord
  from neuron_explainer.api_client import ApiClient
  from neuron_explainer.explanations.few_shot_examples import FewShotExampleSet
  from neuron_explainer.explanations.prompt_builder import HarmonyMessage, PromptBuilder, PromptFormat, Role
  from neuron_explainer.explanations.token_space_few_shot_examples import TokenSpaceFewShotExampleSet
label_desc: Use image node_id 1 to create a new NeuronExplainer object from inherited base classes: ABC with example: obj = NeuronExplainer(model_name, prompt_format, context_size, max_concurrent, cache)