class_docstr: Returns data for rendering ROC and Precision-Recall curves.
code: def config_spec(self) -> types.Spec:
      # If a model is a multiclass classifier, a user can specify which
      # class label to use for plotting the curves. If the label is not
      # specified then the label with index 0 is used by default.
      return {
          TARGET_LABEL_KEY: types.CategoryLabel(),
          TARGET_PREDICTION_KEY: types.SingleFieldMatcher(
              spec="output", types=["MulticlassPreds"], required=False
          ),
      }
  
file_path: lit/lit_nlp/components/curves.py
imports: from collections.abc import Sequence
  from typing import cast, Optional
  from lit_nlp.api import components
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.lib import utils
  import numpy
  from sklearn import metrics
label_desc: Use image node_id 3 for calling the CurvesInterpreter obj's underlying member method code with example usage: obj.config_spec() and returns: dict