code: def default_metrics() -> dict[str, lit_components.Interpreter]:
      return {
          "regression": metrics.RegressionMetrics(),
          "multiclass": metrics.MulticlassMetrics(),
          "multilabel": metrics.MultilabelMetrics(),
          "paired": metrics.MulticlassPairedMetrics(),
          "bleu": metrics.CorpusBLEU(),
          "rouge": metrics.RougeL(),
          "exactmatch": metrics.ExactMatchMetrics(),
      }
  
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 4 for calling a global function with example usage: default_metrics() and returns: dict