

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()



def        __UpperCamelCase     (      _A					:							str		)				->int:



							"""simple docstring"""
							lowerCamelCase_								=lowerCamelCase_								=0
							for letter in data:
														lowerCamelCase_								=(hash_ << 4) + ord(_A		)
														lowerCamelCase_								=hash_ & 0XF0000000
														if x != 0:
																					hash_ ^= x >> 24
														hash_ &= ~x
							return hash_


if __name__ == "__main__":
	import doctest

	doctest.testmod()
