code: 
  
  
  
  """simple docstring"""
  from sklearn.neural_network import MLPClassifier
  
  _lowerCAmelCase					:Dict  =      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
  _lowerCAmelCase					:Optional[int]  =      [0, 1, 0, 0]
  
  
  _lowerCAmelCase					:str  =      MLPClassifier(
      solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
  )
  
  clf.fit(X, y)
  
  
  _lowerCAmelCase					:List[str]  =      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
  _lowerCAmelCase					:List[Any]  =      clf.predict(test)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	int							):
      return list(UpperCamelCase__							)
  
  
  if __name__ == "__main__":
    import doctest
  
    doctest.testmod()
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	int							):
      sequence.append(UpperCamelCase__							)
  
      _UpperCAmelCase							:						str       							=					0
      while sequence[index] != target:
          index += 1
  
      sequence.pop()
  
      if index == len(UpperCamelCase__							):
          return None
  
      return index
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:Dict  =      input('Enter numbers separated by comma:\n').strip()
    _lowerCAmelCase					:List[Any]  =      [int(item) for item in user_input.split(',')]
  
    _lowerCAmelCase					:int  =      input('Enter a single number to be found in the list:\n')
    _lowerCAmelCase					:List[Any]  =      int(target_input)
    _lowerCAmelCase					:Optional[int]  =      sentinel_linear_search(sequence, target)
    if result is not None:
      print(f"{target} found at positions: {result}")
    else:
      print('Not found')
  
  
  