

"""simple docstring"""





from __future__ import annotations




class    _lowerCAmelCase    :







 """simple docstring"""







 def __init__(     self				:     str,   UpperCAmelCase__				:     int      ):
    __lowercase								=		value
    __lowercase								=		None
    __lowercase								=		None







class    _lowerCAmelCase    :







 """simple docstring"""







 __UpperCAmelCase      :   int


 def __init__(     self				:     Union[str, Any]      ):
    __lowercase								=		0


 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     List[str]      ):
    if node is None:
       return

    if path_sum == self.target:
       self.paths += 1

    if node.left:
       self.depth_first_search(node.left,   path_sum + node.left.value      )
    if node.right:
       self.depth_first_search(node.right,   path_sum + node.right.value      )



 def    _lowercase					(     self				:     str,   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     Any = None      ):
    if node is None:
       return 0
    if target is not None:
       __lowercase								=		target

    self.depth_first_search(_SCREAMING_SNAKE_CASE,   node.value      )
    self.path_sum(node.left      )
    self.path_sum(node.right      )

    return self.paths


if __name__ == "__main__":
   import doctest

   doctest.testmod()







from sklearn.neural_network import MLPClassifier

__A	:   Tuple	=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__A	:   Optional[int]	=					[0, 1, 0, 0]


__A	:   int	=					MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__A	:   str	=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__A	:   str	=					clf.predict(test)




def        __UpperCamelCase     (      _A					:							int		)				->Union[str, Any]:



							"""simple docstring"""
							return list(_A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
