parameters: (self, net, input_features, output_blob, s)
docstring: Applies Heaviside step function and Relu / exponentiation to features
          depending on the value of s.
  
          Inputs:
              net -- net with operators
              input_features -- features to processes
              output_blob -- output blob reference
              s -- degree to raise the transformed features
docstring_summary: Applies Heaviside step function and Relu / exponentiation to features
          depending on the value of s.
docstring_tokens: Applies Heaviside step function and Relu / exponentiation to features depending on the value of s .
function: def _heaviside_with_power(self, net, input_features, output_blob, s):
          """
          Applies Heaviside step function and Relu / exponentiation to features
          depending on the value of s.
  
          Inputs:
              net -- net with operators
              input_features -- features to processes
              output_blob -- output blob reference
              s -- degree to raise the transformed features
          """
          if s == 0:
              softsign_features = net.Softsign([input_features],
                                               net.NextScopedBlob('softsign'))
              return net.Relu(softsign_features, output_blob)
          elif s == 1:
              return net.Relu([input_features],
                              output_blob)
          else:
              relu_features = net.Relu([input_features],
                                       net.NextScopedBlob('relu_rand'))
              pow_features = net.Pow([input_features],
                                     net.NextScopedBlob('pow_rand'),
                                     exponent=float(s - 1))
              return net.Mul([relu_features, pow_features],
                             output_blob)
function_tokens: def _heaviside_with_power ( self , net , input_features , output_blob , s ) : if s == 0 : softsign_features = net . Softsign ( [ input_features ] , net . NextScopedBlob ( 'softsign' ) ) return net . Relu ( softsign_features , output_blob ) elif s == 1 : return net . Relu ( [ input_features ] , output_blob ) else : relu_features = net . Relu ( [ input_features ] , net . NextScopedBlob ( 'relu_rand' ) ) pow_features = net . Pow ( [ input_features ] , net . NextScopedBlob ( 'pow_rand' ) , exponent = float ( s - 1 ) ) return net . Mul ( [ relu_features , pow_features ] , output_blob )