

def        __UpperCamelCase     (      _A					:							int = 1000		)				->int:



							"""simple docstring"""
							lowerCamelCase_								=0
							lowerCamelCase_								=0
							while 1:
														num += 3
														if num >= n:
																					break
														total += num
														num += 2
														if num >= n:
																					break
														total += num
														num += 1
														if num >= n:
																					break
														total += num
														num += 3
														if num >= n:
																					break
														total += num
														num += 1
														if num >= n:
																					break
														total += num
														num += 2
														if num >= n:
																					break
														total += num
														num += 3
														if num >= n:
																					break
														total += num
							return total


if __name__ == "__main__":
	print(F"""{solution() = }""")



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()
