code: def convert_elementwise_sub(
      params, w_name, scope_name, inputs, layers, weights, names
  ):
      
      print('Converting elementwise_sub ...')
      model0 = layers[inputs[0]]
      model1 = layers[inputs[1]]
  
      if names == 'short':
          tf_name = 'S' + random_string(7)
      elif names == 'keep':
          tf_name = w_name
      else:
          tf_name = w_name + str(random.random())
  
      sub = keras.layers.Subtract(name=tf_name)
      layers[scope_name] = sub([model0, model1])
docs: Convert elementwise subtraction.
  
      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