parameters: (self, reg_lambda, p_value=0.5)
docstring: reg_lambda: parameter to scale regularization by
  
          p_value:    determines what type of Lp norm to calculate. If p > 0,
                      we will calculate Lp norm with the formula:
                      pow( sum_i { pow(theda_i, p) } ,  1/p)
docstring_summary: reg_lambda: parameter to scale regularization by
docstring_tokens: reg_lambda : parameter to scale regularization by
function: def __init__(self, reg_lambda, p_value=0.5):
          """
          reg_lambda: parameter to scale regularization by
  
          p_value:    determines what type of Lp norm to calculate. If p > 0,
                      we will calculate Lp norm with the formula:
                      pow( sum_i { pow(theda_i, p) } ,  1/p)
          """
          super(LpNorm, self).__init__()
          assert reg_lambda > 0, "factor ahead of regularization should be greater than 0"
          assert p_value > 0, "p_value factor should be greater than 0"
          self.p_value = p_value
          self.reg_lambda = reg_lambda
function_tokens: def __init__ ( self , reg_lambda , p_value = 0.5 ) : super ( LpNorm , self ) . __init__ ( ) assert reg_lambda > 0 , "factor ahead of regularization should be greater than 0" assert p_value > 0 , "p_value factor should be greater than 0" self . p_value = p_value self . reg_lambda = reg_lambda