class_docstr: Abstract class for all `Model` objects in autogluon.timeseries.
  
  Parameters
  ----------
  path : str, default = None
      Directory location to store all outputs.
      If None, a new unique time-stamped directory is chosen.
  freq: str
      Frequency string (cf. gluonts frequency strings) describing the frequency
      of the time series data. For example, "H" for hourly or "D" for daily data.
  prediction_length: int
      Length of the prediction horizon, i.e., the number of time steps the model
      is fit to forecast.
  name : str, default = None
      Name of the subdirectory inside path where model will be saved.
      The final model directory will be os.path.join(path, name)
      If None, defaults to the model's class name: self.__class__.__name__
  metadata: CovariateMetadata
      A mapping of different covariate types known to autogluon.timeseries to column names
      in the data set.
  eval_metric : Union[str, TimeSeriesScorer], default = "WQL"
      Metric by which predictions will be ultimately evaluated on future test data. This only impacts
      ``model.score()``, as eval_metric is not used during training. Available metrics can be found in
      ``autogluon.timeseries.metrics``.
  eval_metric_seasonal_period : int, optional
      Seasonal period used to compute some evaluation metrics such as mean absolute scaled error (MASE). Defaults to
      ``None``, in which case the seasonal period is computed based on the data frequency.
  hyperparameters : dict, default = None
      Hyperparameters that will be used by the model (can be search spaces instead of fixed values).
      If None, model defaults are used. This is identical to passing an empty dictionary.
class_docstr_tok: ["Abstract","class","for","all","`","Model","`","objects","in","autogluon.timeseries",".","Parameters","--","--","--","--","--","path",":","str",",","default","=","None","Directory","location","to","store","all","outputs",".","If","None",",","a","new","unique","time-stamped","directory","is","chosen",".","freq",":","str","Frequency","string","(","cf",".","gluonts","frequency","strings",")","describing","the","frequency","of","the","time","series","data",".","For","example",",","``","H","''","for","hourly","or","``","D","''","for","daily","data",".","prediction_length",":","int","Length","of","the","prediction","horizon",",","i.e.",",","the","number","of","time","steps","the","model","is","fit","to","forecast",".","name",":","str",",","default","=","None","Name","of","the","subdirectory","inside","path","where","model","will","be","saved",".","The","final","model","directory","will","be","os.path.join","(","path",",","name",")","If","None",",","defaults","to","the","model","'s","class","name",":","self.__class__.__name__","metadata",":","CovariateMetadata","A","mapping","of","different","covariate","types","known","to","autogluon.timeseries","to","column","names","in","the","data","set",".","eval_metric",":","Union","[","str",",","TimeSeriesScorer","]",",","default","=","``","WQL","''","Metric","by","which","predictions","will","be","ultimately","evaluated","on","future","test","data",".","This","only","impacts","``","model.score","(",")","``",",","as","eval_metric","is","not","used","during","training",".","Available","metrics","can","be","found","in","``","autogluon.timeseries.metrics","``",".","eval_metric_seasonal_period",":","int",",","optional","Seasonal","period","used","to","compute","some","evaluation","metrics","such","as","mean","absolute","scaled","error","(","MASE",")",".","Defaults","to","``","None","``",",","in","which","case","the","seasonal","period","is","computed","based","on","the","data","frequency",".","hyperparameters",":","dict",",","default","=","None","Hyperparameters","that","will","be","used","by","the","model","(","can","be","search","spaces","instead","of","fixed","values",")",".","If","None",",","model","defaults","are","used",".","This","is","identical","to","passing","an","empty","dictionary","."]
docstr: Load the cached OOF predictions from disk.
docstr_tok: ["Load","the","cached","OOF","predictions","from","disk","."]
code: def load_oof_predictions(
      cls, path: str, verbose: bool = True
  ) -> List[TimeSeriesDataFrame]:
      """Load the cached OOF predictions from disk."""
      return load_pkl.load(
          path=os.path.join(path, "utils", cls._oof_filename),
          verbose=verbose,
      )
  
imports: import logging
  import os
  import re
  import time
  from contextlib import nullcontext
  from typing import Any, Dict, List, Optional, Union
  from autogluon.common import space
  from autogluon.common.loaders import load_pkl
  from autogluon.common.savers import save_pkl
  from autogluon.core.hpo.exceptions import EmptySearchSpace
  from autogluon.core.hpo.executors import HpoExecutor, RayHpoExecutor
  from autogluon.core.models import AbstractModel
  from autogluon.timeseries.dataset import TimeSeriesDataFrame
  from autogluon.timeseries.metrics import TimeSeriesScorer, check_get_evaluation_metric
  from autogluon.timeseries.utils.features import CovariateMetadata
  from autogluon.timeseries.utils.warning_filters import disable_stdout, warning_filter
  from .model_trial import model_trial, skip_hpo
label_desc: Use image node_id 5 for calling the AbstractTimeSeriesModel obj's underlying member method code with example usage: obj.load_oof_predictions(cls, path, verbose) and returns: load_pkl