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: Generate the sketches.
  This python interface is mainly used for debugging and testing.
  The actual search is all done in c++.
  
  Parameters
  ----------
  print_for_debug : bool = False
      Whether print out the sketches for debug.
  
  Returns
  -------
  sketches : List[State]
      The generated sketches of this search task.
docstr_tok: ["Generate","the","sketches",".","This","python","interface","is","mainly","used","for","debugging","and","testing",".","The","actual","search","is","all","done","in","c++",".","Parameters","--","--","--","--","--","print_for_debug",":","bool","=","False","Whether","print","out","the","sketches","for","debug",".","Returns","--","--","--","-","sketches",":","List","[","State","]","The","generated","sketches","of","this","search","task","."]
code: def generate_sketches(self, print_for_debug=False):
      """Generate the sketches.
      This python interface is mainly used for debugging and testing.
      The actual search is all done in c++.
  
      Parameters
      ----------
      print_for_debug : bool = False
          Whether print out the sketches for debug.
  
      Returns
      -------
      sketches : List[State]
          The generated sketches of this search task.
      """
      sketches = _ffi_api.SketchPolicyGenerateSketches(self)
      if print_for_debug:
          for i, s in enumerate(sketches):
              print("=" * 20 + f" {i} " + "=" * 20)
              print(s)
      return sketches
  
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 2 for calling the SketchPolicy obj's underlying member method code with example usage: obj.generate_sketches(print_for_debug) and returns: sketches