code: def _build_layers(self, inputs, num_outputs, options):
          
  
          hiddens = options.get("fcnet_hiddens")
          activation = get_activation_fn(options.get("fcnet_activation"))
  
          with tf.name_scope("fc_net"):
              i = 1
              last_layer = inputs
              for size in hiddens:
                  label = "fc{}".format(i)
                  last_layer = slim.fully_connected(
                      last_layer,
                      size,
                      weights_initializer=normc_initializer(1.0),
                      activation_fn=activation,
                      scope=label)
                  i += 1
              label = "fc_out"
              output = slim.fully_connected(
                  last_layer,
                  num_outputs,
                  weights_initializer=normc_initializer(0.01),
                  activation_fn=None,
                  scope=label)
              return output, last_layer
docs: Process the flattened inputs.
  
          Note that dict inputs will be flattened into a vector. To define a
          model that processes the components separately, use _build_layers_v2().