class_docstr: Random cost model
  
  Parameters
  ----------
  random_state : Union[Tuple[str, np.ndarray, int, int, float], dict]
      The random state of the random number generator.
  path : Optional[str]
      The path of the random cost model.
  max_range : Optional[int]
      The maximum range of random results, [0, max_range].
  
  Reference
  ---------
  https://numpy.org/doc/stable/reference/random/generated/numpy.random.get_state.html
class_docstr_tok: ["Random","cost","model","Parameters","--","--","--","--","--","random_state",":","Union","[","Tuple","[","str",",","np.ndarray",",","int",",","int",",","float","]",",","dict","]","The","random","state","of","the","random","number","generator",".","path",":","Optional","[","str","]","The","path","of","the","random","cost","model",".","max_range",":","Optional","[","int","]","The","maximum","range","of","random","results",",","[","0",",","max_range","]",".","Reference","--","--","--","--","-","https",":","\/\/numpy.org\/doc\/stable\/reference\/random\/generated\/numpy.random.get_state.html"]
code: def __init__(
      self,
      *,
      seed: Optional[int] = None,
      path: Optional[str] = None,
      max_range: Optional[int] = 100,
  ):
      import numpy as np  # type: ignore # pylint: disable=import-outside-toplevel
  
      super().__init__()
      if path is not None:
          self.load(path)
      else:
          np.random.seed(seed)
          self.random_state = np.random.get_state()
      self.max_range = max_range
  
file_path: tvm/python/tvm/meta_schedule/cost_model/random_model.py
imports: from typing import List, Optional, Tuple, Union
  from ..cost_model import PyCostModel
  from ..runner import RunnerResult
  from ..search_strategy import MeasureCandidate
  from ..tune_context import TuneContext
  from ..utils import derived_object
label_desc: Use image node_id 1 to create a new RandomModel object from inherited base classes: PyCostModel with example: obj = RandomModel()