code: 
  
  
  from sklearn.neural_network import MLPClassifier
  
  UpperCAmelCase      :						str    =		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
  UpperCAmelCase      :						Union[str, Any]    =		[0, 1, 0, 0]
  
  
  UpperCAmelCase      :						str    =		MLPClassifier(
      solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
  )
  
  clf.fit(X, y)
  
  
  UpperCAmelCase      :						str    =		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
  UpperCAmelCase      :						Optional[int]    =		clf.predict(test)
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return list(lowerCamelCase__			)
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
style_context: 
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 100     ,       lowerCamelCase__		:	int = 100			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return max(
  			    sum(int(lowerCamelCase__			) for x in str(base**power			)			)
  			    for base in range(lowerCamelCase__			)
  			    for power in range(lowerCamelCase__			)			)
  
  
  # Tests
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  