class_docstr: Toy PBT problem for benchmarking adaptive learning rate.
  
  The goal is to optimize this trainable's accuracy. The accuracy increases
  fastest at the optimal lr, which is a function of the current accuracy.
  
  The optimal lr schedule for this problem is the triangle wave as follows.
  Note that many lr schedules for real models also follow this shape:
  
   best lr
    ^
    |    /      |   /        |  /          | /            ------------> accuracy
  
  In this problem, using PBT with a population of 2-4 is sufficient to
  roughly approximate this lr schedule. Higher population sizes will yield
  faster convergence. Training will not converge without PBT.
class_docstr_tok: ["Toy","PBT","problem","for","benchmarking","adaptive","learning","rate",".","The","goal","is","to","optimize","this","trainable","'s","accuracy",".","The","accuracy","increases","fastest","at","the","optimal","lr",",","which","is","a","function","of","the","current","accuracy",".","The","optimal","lr","schedule","for","this","problem","is","the","triangle","wave","as","follows",".","Note","that","many","lr","schedules","for","real","models","also","follow","this","shape",":","best","lr","^","|","\/","|","\/","|","\/","|","\/","--","--","--","--","--","--",">","accuracy","In","this","problem",",","using","PBT","with","a","population","of","2-4","is","sufficient","to","roughly","approximate","this","lr","schedule",".","Higher","population","sizes","will","yield","faster","convergence",".","Training","will","not","converge","without","PBT","."]
code: def save_checkpoint(self, checkpoint_dir):
      return {
          "accuracy": self.accuracy,
          "lr": self.lr,
      }
  
file_path: ray/python/ray/tune/examples/pbt_example.py
imports: import numpy
  import argparse
  import random
  import ray
  from ray import train, tune
  from ray.tune.schedulers import PopulationBasedTraining
label_desc: Use image node_id 3 for calling the PBTBenchmarkExample obj's underlying member method code with example usage: obj.save_checkpoint(checkpoint_dir) and returns: dict