class_docstr: Callable for pruning archives in the optimizer class.
  See Optimizer.pruning attribute, called at each "tell".
  
  Parameters
  ----------
  min_len: int
      minimum length of the pruned archive.
  max_len: int
      length at which pruning is activated (maximum allowed length for the archive).
  
  Note
  ----
  For each of the 3 criteria (optimistic, pessimistic and average), the min_len best (lowest)
  points will be kept, which can lead to at most 3 * min_len points.
code: def __init__(self, min_len: int, max_len: int):
      self.min_len = min_len
      self.max_len = max_len
      self._num_prunings = 0
  
imports: import math
  import operator
  import numpy
  from nevergrad.parametrization import parameter
  from nevergrad.parametrization.utils import float_penalty
  import nevergrad.common.typing
  from nevergrad.common.tools import OrderedSet
label_desc: Use image node_id 1 to create a new Pruning object  with example: obj = Pruning(min_len, max_len)