code: def required_interpreters() -> dict[str, lit_components.Interpreter]:
      """Returns a dict of required interpreters.
  
      These are used by multiple core modules, and without them the frontend will
      likely throw errors.
      """
      # Ensure the prediction analysis interpreters are included.
      prediction_analysis_interpreters: dict[
          str, lit_components.Interpreter
      ] = {
          "classification": classification_results.ClassificationInterpreter(),
          "regression": regression_results.RegressionInterpreter(),
      }
      return prediction_analysis_interpreters
  
imports: from lit_nlp.api import components
  from lit_nlp.api import model
  from lit_nlp.components import ablation_flip
  from lit_nlp.components import classification_results
  from lit_nlp.components import curves
  from lit_nlp.components import gradient_maps
  from lit_nlp.components import hotflip
  from lit_nlp.components import lime_explainer
  from lit_nlp.components import metrics
  from lit_nlp.components import model_salience
  from lit_nlp.components import nearest_neighbors
  from lit_nlp.components import pca
  from lit_nlp.components import pdp
  from lit_nlp.components import projection
  from lit_nlp.components import regression_results
  from lit_nlp.components import salience_clustering
  from lit_nlp.components import scrambler
  from lit_nlp.components import tcav
  from lit_nlp.components import thresholder
  from lit_nlp.components import word_replacer
label_desc: Use image node_id 2 for calling a global function with example usage: required_interpreters() and returns: prediction_analysis_interpreters