class_docstr: The search policy that searches in a hierarchical search space defined by sketches.
  The policy randomly samples programs from the space defined by sketches and use evolutionary
  search to fine-tune them.
  
  Parameters
  ----------
  task : SearchTask
      The SearchTask for the computation declaration.
  program_cost_model : CostModel = RandomModel()
      The cost model to estimate the complete schedules.
  params : Optional[Dict[str, Any]]
      Parameters of the search policy.
      See `src/auto_scheduler/search_policy/sketch_search_policy.h` for the definitions.
      See `DEFAULT_PARAMS` below to find the default values.
  seed : Optional[int]
      Random seed.
  verbose : int = 1
      Verbosity level. 0 for silent, 1 to output information during schedule search.
  init_search_callbacks : Optional[List[SearchCallback]]
      Callback functions called before the search process, usually used to do extra
      initializations.
      Possible callbacks:
  
        - auto_scheduler.PreloadMeasuredStates
        - auto_scheduler.PreloadCustomSketchRule
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docstr: Perform evolutionary search.
  This python interface is mainly used for debugging and testing.
  The actual search is all done in c++.
  
  Parameters
  ----------
  init_populations: List[State]
      The initial population states
  out_size : int
      The size of generated states
  
  Returns
  -------
  states: List[State]
      The generated states
docstr_tok: ["Perform","evolutionary","search",".","This","python","interface","is","mainly","used","for","debugging","and","testing",".","The","actual","search","is","all","done","in","c++",".","Parameters","--","--","--","--","--","init_populations",":","List","[","State","]","The","initial","population","states","out_size",":","int","The","size","of","generated","states","Returns","--","--","--","-","states",":","List","[","State","]","The","generated","states"]
code: def evolutionary_search(self, init_populations, out_size):
      """Perform evolutionary search.
      This python interface is mainly used for debugging and testing.
      The actual search is all done in c++.
  
      Parameters
      ----------
      init_populations: List[State]
          The initial population states
      out_size : int
          The size of generated states
  
      Returns
      -------
      states: List[State]
          The generated states
      """
      states = _ffi_api.SketchPolicyEvolutionarySearch(
          self, init_populations, out_size
      )
      return states
  
imports: import random
  import tvm._ffi
  from tvm.runtime import Object
  from .cost_model import RandomModel
  from .None import _ffi_api
label_desc: Use image node_id 4 for calling the SketchPolicy obj's underlying member method code with example usage: obj.evolutionary_search(init_populations, out_size) and returns: states