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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _build_layers(self, inputs, num_outputs, options):
 """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(). """


        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