class_docstr: parallel mapped optimization starting from N uniform randomly sampled points
      
class_docstr_tok: ["parallel","mapped","optimization","starting","from","N","uniform","randomly","sampled","points"]
docstr: Takes two initial inputs: 
      dim   -- dimensionality of the problem
      npts  -- number of parallel solver instances
  
  All important class members are inherited from AbstractEnsembleSolver.
          
docstr_tok: ["Takes","two","initial","inputs",":","dim","--","dimensionality","of","the","problem","npts","--","number","of","parallel","solver","instances","All","important","class","members","are","inherited","from","AbstractEnsembleSolver","."]
code: def __init__(self, dim, npts=8):
      """
      Takes two initial inputs:
          dim   -- dimensionality of the problem
          npts  -- number of parallel solver instances
  
      All important class members are inherited from AbstractEnsembleSolver.
      """
      super(BuckshotSolver, self).__init__(dim, npts=npts)
      from mystic.termination import NormalizedChangeOverGeneration
  
      convergence_tol = 1e-4
      self._termination = NormalizedChangeOverGeneration(
          convergence_tol
      )
  
file_path: mystic/mystic/ensemble.py
imports: from mystic.tools import unpair
  from mystic.abstract_ensemble_solver import AbstractEnsembleSolver
label_desc: Use image node_id 1 to create a new BuckshotSolver object from inherited base classes: AbstractEnsembleSolver with example: obj = BuckshotSolver(dim, npts)