






def     lowerCamelCase__				(   __lowerCamelCase      :					list		):
  __UpperCAmelCase							:     Dict									=  len(__lowerCamelCase		)

  # Set the gap value to be decreased by a factor of 1.3
  # after each iteration
  __UpperCAmelCase							:     Optional[Any]									=  1.3

  # Continue sorting until the gap is 1
  while gap > 1:
    # Decrease the gap value
    __UpperCAmelCase							:     Tuple									=  int(gap / shrink		)

    # Sort the elements using insertion sort
    for i in range(__lowerCamelCase  ,		len(__lowerCamelCase		)		):
      __UpperCAmelCase							:     List[Any]									=  collection[i]
      __UpperCAmelCase							:     Optional[int]									=  i
      while j >= gap and collection[j - gap] > temp:
        __UpperCAmelCase							:     List[Any]									=  collection[j - gap]
        j -= gap
      __UpperCAmelCase							:     Optional[int]									=  temp

  return collection


if __name__ == "__main__":
       import doctest

       doctest.testmod()










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


