class_docstr: Returns data for rendering ROC and Precision-Recall curves.
docstr: Returns the list of supported prediction keys in the model output.
  
  Args:
    output_spec: The model output specification.
  
  Returns:
    The list of keys.
docstr_tok: ["Returns","the","list","of","supported","prediction","keys","in","the","model","output",".","Args",":","output_spec",":","The","model","output","specification",".","Returns",":","The","list","of","keys","."]
code: def _find_supported_pred_keys(
      self, output_spec: types.Spec
  ) -> list[str]:
      """Returns the list of supported prediction keys in the model output.
  
      Args:
        output_spec: The model output specification.
  
      Returns:
        The list of keys.
      """
      all_keys = lit_utils.find_spec_keys(
          output_spec, types.MulticlassPreds
      )
      supported_keys = [
          k
          for k in all_keys
          if cast(types.MulticlassPreds, output_spec[k]).parent
      ]
      return supported_keys
  
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 5 for calling the CurvesInterpreter obj's underlying member method code with example usage: obj._find_supported_pred_keys(output_spec) and returns: supported_keys