code: def convert_relu(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting relu ...')
  
      if names == 'short':
          tf_name = 'RELU' + random_string(4)
      elif names == 'keep':
          tf_name = w_name
      else:
          tf_name = w_name + str(random.random())
  
      relu = keras.layers.Activation('relu', name=tf_name)
      layers[scope_name] = relu(layers[inputs[0]])
docs: Convert relu layer.
  
      Args:
          params: dictionary with layer parameters
          w_name: name prefix in state_dict
          scope_name: pytorch scope name
          inputs: pytorch node inputs
          layers: dictionary with keras tensors
          weights: pytorch state_dict
          names: use short names for keras layers