
"""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()





"""simple docstring"""

UpperCamelCase      :							int          =					1_0**8






def 				A		(							snake_case :int						) ->      bool:
 if n % 1_0 == 0:
  return False
 __UpperCamelCase							    =			str(snake_case						)
 return s == s[::-1]






def 				A		(							) ->      int:
 __UpperCamelCase							    =			set()
 __UpperCamelCase							    =			1
 __UpperCamelCase							    =			5
 while sum_squares < LIMIT:
  __UpperCamelCase							    =			first_square + 1
  while sum_squares < LIMIT:
   if is_palindrome(snake_case						):
    answer.add(snake_case						)
   last_square += 1
   sum_squares += last_square**2
  first_square += 1
  __UpperCamelCase							    =			first_square**2 + (first_square + 1) ** 2

 return sum(snake_case						)


if __name__ == "__main__":
      print(solution())



