code: def convert_constant(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting constant ...')
  
      params_list = params['value'].numpy()
  
      def target_layer(x, value=params_list):
          return tf.constant(value.tolist(), shape=value.shape)
  
      lambda_layer = keras.layers.Lambda(target_layer)
      layers[scope_name + '_np'] = params_list  # ad-hoc
      layers[scope_name] = lambda_layer(layers[list(layers.keys())[0]])
docs: Convert constant 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