parameters: (
          self,
          parameters: List[Union[FuzzedParameter, List[FuzzedParameter]]],
          tensors: List[Union[FuzzedTensor, List[FuzzedTensor]]],
          constraints: Optional[List[Callable]] = None,
          seed: Optional[int] = None
      )
docstring: Args:
              parameters:
                  List of FuzzedParameters which provide specifications
                  for generated parameters. Iterable elements will be
                  unpacked, though arbitrary nested structures will not.
              tensors:
                  List of FuzzedTensors which define the Tensors which
                  will be created each step based on the parameters for
                  that step. Iterable elements will be unpacked, though
                  arbitrary nested structures will not.
              constraints:
                  List of callables. They will be called with params
                  as kwargs, and if any of them return False the current
                  set of parameters will be rejected.
              seed:
                  Seed for the RandomState used by the Fuzzer. This will
                  also be used to set the PyTorch random seed so that random
                  ops will create reproducible Tensors.
docstring_summary: Args:
              parameters:
                  List of FuzzedParameters which provide specifications
                  for generated parameters. Iterable elements will be
                  unpacked, though arbitrary nested structures will not.
              tensors:
                  List of FuzzedTensors which define the Tensors which
                  will be created each step based on the parameters for
                  that step. Iterable elements will be unpacked, though
                  arbitrary nested structures will not.
              constraints:
                  List of callables. They will be called with params
                  as kwargs, and if any of them return False the current
                  set of parameters will be rejected.
              seed:
                  Seed for the RandomState used by the Fuzzer. This will
                  also be used to set the PyTorch random seed so that random
                  ops will create reproducible Tensors.
docstring_tokens: Args : parameters : List of FuzzedParameters which provide specifications for generated parameters . Iterable elements will be unpacked though arbitrary nested structures will not . tensors : List of FuzzedTensors which define the Tensors which will be created each step based on the parameters for that step . Iterable elements will be unpacked though arbitrary nested structures will not . constraints : List of callables . They will be called with params as kwargs and if any of them return False the current set of parameters will be rejected . seed : Seed for the RandomState used by the Fuzzer . This will also be used to set the PyTorch random seed so that random ops will create reproducible Tensors .
function: def __init__(
          self,
          parameters: List[Union[FuzzedParameter, List[FuzzedParameter]]],
          tensors: List[Union[FuzzedTensor, List[FuzzedTensor]]],
          constraints: Optional[List[Callable]] = None,
          seed: Optional[int] = None
      ):
          """
          Args:
              parameters:
                  List of FuzzedParameters which provide specifications
                  for generated parameters. Iterable elements will be
                  unpacked, though arbitrary nested structures will not.
              tensors:
                  List of FuzzedTensors which define the Tensors which
                  will be created each step based on the parameters for
                  that step. Iterable elements will be unpacked, though
                  arbitrary nested structures will not.
              constraints:
                  List of callables. They will be called with params
                  as kwargs, and if any of them return False the current
                  set of parameters will be rejected.
              seed:
                  Seed for the RandomState used by the Fuzzer. This will
                  also be used to set the PyTorch random seed so that random
                  ops will create reproducible Tensors.
          """
          if seed is None:
              seed = np.random.RandomState().randint(0, 2**63)
          self._seed = seed
          self._parameters = Fuzzer._unpack(parameters, FuzzedParameter)
          self._tensors = Fuzzer._unpack(tensors, FuzzedTensor)
          self._constraints = constraints or ()
  
          p_names = {p.name for p in self._parameters}
          t_names = {t.name for t in self._tensors}
          name_overlap = p_names.intersection(t_names)
          if name_overlap:
              raise ValueError(f"Duplicate names in parameters and tensors: {name_overlap}")
  
          self._rejections = 0
          self._total_generated = 0
function_tokens: def __init__ ( self , parameters : List [ Union [ FuzzedParameter , List [ FuzzedParameter ] ] ] , tensors : List [ Union [ FuzzedTensor , List [ FuzzedTensor ] ] ] , constraints : Optional [ List [ Callable ] ] = None , seed : Optional [ int ] = None ) : if seed is None : seed = np . random . RandomState ( ) . randint ( 0 , 2 ** 63 ) self . _seed = seed self . _parameters = Fuzzer . _unpack ( parameters , FuzzedParameter ) self . _tensors = Fuzzer . _unpack ( tensors , FuzzedTensor ) self . _constraints = constraints or ( ) p_names = { p . name for p in self . _parameters } t_names = { t . name for t in self . _tensors } name_overlap = p_names . intersection ( t_names ) if name_overlap : raise ValueError ( f"Duplicate names in parameters and tensors: {name_overlap}" ) self . _rejections = 0 self . _total_generated = 0