code: def convert_elementwise_add(
      params, w_name, scope_name, inputs, layers, weights, names
  ):
      
      print('Converting elementwise_add ...')
      if 'broadcast' in params:
          model0 = layers[inputs[0]]
          model1 = layers[inputs[1]]
  
          if names == 'short':
              tf_name = 'A' + random_string(7)
          elif names == 'keep':
              tf_name = w_name
          else:
              tf_name = w_name + str(random.random())
  
          def target_layer(x):
              layer = tf.add(x[0], x[1])
              return layer
  
          lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
          layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]])
      else:
          model0 = layers[inputs[0]]
          model1 = layers[inputs[1]]
  
          if names == 'short':
              tf_name = 'A' + random_string(7)
          elif names == 'keep':
              tf_name = w_name
          else:
              tf_name = w_name + str(random.random())
  
          add = keras.layers.Add(name=tf_name)
          layers[scope_name] = add([model0, model1])
docs: Convert elementwise addition.
  
      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