class_docstr: Population-Based Training, also known as Lamarckism or Meta-Optimization.
code: def unflatten(self, x):
      y = []
      current_idx = 0
      for i in range(self._hyperparameter_dimension):
          y += [x[current_idx : (current_idx + self._dimensions[i])]]
          current_idx += self._dimensions[i]
      assert current_idx == len(x) == self._total_dimension
      return y
  
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 2 for calling the PBT obj's underlying member method code with example usage: obj.unflatten(x) and returns: y