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: Load the predictions for this run
  
  Parameters
  ----------
  kind : "ensemble" | "test"
      The kind of predictions to load
  
  precisions : type | None = None
      What kind of precision reduction to apply
  
  Returns
  -------
  np.ndarray
      The loaded predictions
docstr_tok: ["Load","the","predictions","for","this","run","Parameters","--","--","--","--","--","kind",":","``","ensemble","''","|","``","test","''","The","kind","of","predictions","to","load","precisions",":","type","|","None","=","None","What","kind","of","precision","reduction","to","apply","Returns","--","--","--","-","np.ndarray","The","loaded","predictions"]
code: def predictions(
      self,
      kind: str = "ensemble",
      precision: int | None = None,
  ) -> np.ndarray:
      """Load the predictions for this run
  
      Parameters
      ----------
      kind : "ensemble" | "test"
          The kind of predictions to load
  
      precisions : type | None = None
          What kind of precision reduction to apply
  
      Returns
      -------
      np.ndarray
          The loaded predictions
      """
      key = f"predictions_{kind}"
      if key in self._cache:
          return self._cache[key]
  
      path = self.pred_path(kind)
  
      with path.open("rb") as f:
          # TODO: We should probably remove this requirement. I'm not sure why model
          # predictions are being saved as pickled
          predictions = np.load(f, allow_pickle=True)
  
      if precision:
          dtypes: dict[int, type] = {
              16: np.float16,
              32: np.float32,
              64: np.float64,
          }
          dtype = dtypes.get(precision, None)
  
          if dtype is not None:
              predictions = predictions.astype(dtype=dtype, copy=False)
  
      self._cache[key] = predictions
      return predictions
  
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 8 for calling the Run obj's underlying member method code with example usage: obj.predictions(kind, precision) and returns: predictions, self