class_docstr: Population-Based Training, also known as Lamarckism or Meta-Optimization.
code: def evolve(self, x: np.ndarray, pp: np.ndarray):
      assert len(pp) == self._hyperparameter_dimension
  
      # We define a gradient operator.
      def gradient(f, x):
          epsilon = 1e-15
          # We compute a gradient by finite differences.
          g = np.zeros(len(x))
          value_minus = f(x)
          assert isinstance(value_minus, float), str(type(value_minus))
          for i in range(len(x)):
              e = np.zeros(len(x))
              e[i] = epsilon
              value_plus = f(x + e)
              assert isinstance(value_plus, float), str(
                  type(value_plus)
              )
              g[i] = (value_plus - value_minus) / epsilon
          return g
  
      y = self.unflatten(x)
      assert len(y) == self._hyperparameter_dimension
  
      # There are self._hyperparameter_dimension learning rates.
      # Each of them is responsible of one gradient descent.
      for j in range(self._hyperparameter_dimension):
          assert len(y[j]) == self._dimensions[j]
          assert isinstance(self._funcs[j](y[j]), float), str(
              type(self._funcs[j](y[j]))
          )
          y[j] -= np.exp(pp[j]) * (
              gradient(self._funcs[j], y[j] - self._optima[j])
              + np.random.normal(self._dimensions[j])
          )
      current_idx = 0
      for j in range(self._hyperparameter_dimension):
          x[current_idx : (current_idx) + self._dimensions[j]] = y[j]
          current_idx += self._dimensions[j]
      assert current_idx == self._total_dimension
  
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 4 for calling the PBT obj's underlying member method code with example usage: obj.evolve(x, pp) and returns: g