



from itertools import combinations_with_replacement






def        a( A			:       int = 100			)						->			int:
					"""simple docstring"""





					a   	=  [*list(range(1					,	21			)			), 25]
					a   	=  [2 * x for x in range(1					,	21			)] + [50]
					a   	=  [3 * x for x in range(1					,	21			)]
					a   	=  singles + doubles + triples + [0]

					a   	=  0
					a   	=  42
					a   	=  42
					a   	=  42
					a   	=  42

					for double in doubles:
										for throwa, throwa in combinations_with_replacement(A					,	2			):
															a   	=  double + throwa + throwa
															if checkout_total < limit:
																				num_checkouts += 1

					return num_checkouts


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


"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



