class_docstr: Population-Based Training, also known as Lamarckism or Meta-Optimization.
code: def itercases(cls) -> tp.Iterator["PBT"]:
      options = dict(
          names=[
              ["sphere", "cigar", "ellipsoid", "cigar"],
              ["hm", "rastrigin", "sphere", "cigar"],
          ],
          dimensions=[[2, 2, 2, 2], [5, 5, 5, 5], [77, 77, 77, 77]],
      )
      keys = sorted(options)
      select = itertools.product(*(options[k] for k in keys))  # type: ignore
      cases = (dict(zip(keys, s)) for s in select)
      return (cls(**c) for c in cases)
  
file_path: nevergrad/nevergrad/functions/pbt.py
imports: import itertools
  import typing
  import numpy
  from nevergrad.parametrization import parameter
  from .base import ExperimentFunction
  from .None import corefuncs
label_desc: Use image node_id 6 for calling the PBT obj's underlying member method code with example usage: obj.itercases(cls) and returns: unknown