class_docstr: Class for storing information about a run used during ensemble building.
  
  Note
  ----
  This is for internal use by the EnsembleBuilder and not for general usage.
class_docstr_tok: ["Class","for","storing","information","about","a","run","used","during","ensemble","building",".","Note","--","--","This","is","for","internal","use","by","the","EnsembleBuilder","and","not","for","general","usage","."]
docstr: Parameters
  ----------
  kind: "ensemble" | "test" = "ensemble"
      The kind of predictions to query for
  
  Returns
  -------
  bool
      Whether this run has the kind of predictions queried for
docstr_tok: ["Parameters","--","--","--","--","--","kind",":","``","ensemble","''","|","``","test","''","=","``","ensemble","''","The","kind","of","predictions","to","query","for","Returns","--","--","--","-","bool","Whether","this","run","has","the","kind","of","predictions","queried","for"]
code: def has_predictions(self, kind: str = "ensemble") -> bool:
      """
      Parameters
      ----------
      kind: "ensemble" | "test" = "ensemble"
          The kind of predictions to query for
  
      Returns
      -------
      bool
          Whether this run has the kind of predictions queried for
      """
      return self.pred_path(kind).exists()
  
imports: from __future__ import annotations
  from typing import Tuple
  import re
  from pathlib import Path
  import numpy
  from autosklearn.util.disk import sizeof
label_desc: Use image node_id 7 for calling the Run obj's underlying member method code with example usage: obj.has_predictions(kind) and returns: self