class_docstr: AbstractEnsembleSolver base class for mystic optimizers that are called within
  a parallel map.  This allows pseudo-global coverage of parameter space using
  non-global optimizers.
      
class_docstr_tok: ["AbstractEnsembleSolver","base","class","for","mystic","optimizers","that","are","called","within","a","parallel","map",".","This","allows","pseudo-global","coverage","of","parameter","space","using","non-global","optimizers","."]
docstr: Generate Random Initial Points from Distribution (dist)
  
  Args:
      dist (Distribution, default=None): a mystic.math.Distribution instance
  
  Returns:
      None
  
  Notes:
      - if ``dist`` is None, use a uniform distribution in the interval ``[0, 1)``
  
  NOTE:
      *** this method must be overwritten ***
docstr_tok: ["Generate","Random","Initial","Points","from","Distribution","(","dist",")","Args",":","dist","(","Distribution",",","default=None",")",":","a","mystic.math.Distribution","instance","Returns",":","None","Notes",":","-","if","``","dist","``","is","None",",","use","a","uniform","distribution","in","the","interval","``","[","0",",","1",")","``","NOTE",":","*","*","*","this","method","must","be","overwritten","*","*","*"]
code: def SetSampledInitialPoints(self, dist=None):
      """Generate Random Initial Points from Distribution (dist)
  
      Args:
          dist (Distribution, default=None): a mystic.math.Distribution instance
  
      Returns:
          None
  
      Notes:
          - if ``dist`` is None, use a uniform distribution in the interval ``[0, 1)``
  
      NOTE:
          *** this method must be overwritten ***"""
      raise NotImplementedError("must be overwritten...")
  
imports: from mystic.monitors import Null
  from mystic.abstract_map_solver import AbstractMapSolver
  from mystic.tools import wrap_function
  from functools import reduce
label_desc: Use image node_id 13 for calling the AbstractEnsembleSolver obj's underlying member method code with example usage: obj.SetSampledInitialPoints(dist) without return types