class_docstr: Example agent whose learning curve is a random sigmoid.
  
  The dummy hyperparameters "width" and "height" determine the slope and
  maximum reward value reached.
class_docstr_tok: ["Example","agent","whose","learning","curve","is","a","random","sigmoid",".","The","dummy","hyperparameters","``","width","''","and","``","height","''","determine","the","slope","and","maximum","reward","value","reached","."]
code: def load_checkpoint(self, checkpoint_dir):
      path = os.path.join(checkpoint_dir, "checkpoint")
      with open(path, "r") as f:
          self.timestep = json.loads(f.read())["timestep"]
  
file_path: ray/python/ray/tune/examples/bohb_example.py
imports: import json
  import time
  import os
  import numpy
  import ray
  from ray import train, tune
  from ray.tune import Trainable
  from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
  from ray.tune.search.bohb import TuneBOHB
label_desc: Use image node_id 4 for calling the MyTrainableClass obj's underlying member method code with example usage: obj.load_checkpoint(checkpoint_dir) without return types