class_docstr: Returns data for rendering ROC and Precision-Recall curves.
docstr: True if using a classification model and dataset has ground truth.
docstr_tok: ["True","if","using","a","classification","model","and","dataset","has","ground","truth","."]
code: def is_compatible(
      self, model: lit_model.Model, dataset: lit_dataset.Dataset
  ) -> bool:
      """True if using a classification model and dataset has ground truth."""
      output_spec = model.output_spec()
      supported_keys = self._find_supported_pred_keys(output_spec)
      has_parents = all(
          cast(types.MulticlassPreds, output_spec[key]).parent
          in dataset.spec()
          for key in supported_keys
      )
      return bool(supported_keys) and has_parents
  
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 2 for calling the CurvesInterpreter obj's underlying member method code with example usage: obj.is_compatible(model, dataset) and returns: unknown