how to use neat's feed forward network by itself?

I want to use NEAT's neural network by itself so I can control how the selection of the fittest(training) is done along with other things, but the source code for NEAT's feed forward network can't be used alone(to create one I have to have a config file, and a genome).

Source code for nn.feed_forward:

from neat.graphs import feed_forward_layersclass FeedForwardNetwork(object):    def __init__(self, inputs, outputs, node_evals):        self.input_nodes = inputs        self.output_nodes = outputs        self.node_evals = node_evals        self.values = dict((key, 0.0) for key in inputs + outputs)    def activate(self, inputs):        if len(self.input_nodes) != len(inputs):            raise RuntimeError("Expected {0:n} inputs, got {1:n}".format(len(self.input_nodes), len(inputs)))        for k, v in zip(self.input_nodes, inputs):            self.values[k] = v        for node, act_func, agg_func, bias, response, links in self.node_evals:            node_inputs = []            for i, w in links:                node_inputs.append(self.values[i] * w)            s = agg_func(node_inputs)            self.values[node] = act_func(bias + response * s)        return [self.values[i] for i in self.output_nodes]    @staticmethod    def create(genome, config): # I don't want to have to pass in either arguments to create a network""" Receives a genome and returns its phenotype (a FeedForwardNetwork). """        # Gather expressed connections.        connections = [cg.key for cg in genome.connections.values() if cg.enabled]        layers = feed_forward_layers(config.genome_config.input_keys, config.genome_config.output_keys, connections)        node_evals = []        for layer in layers:            for node in layer:                inputs = []                for conn_key in connections:                    inode, onode = conn_key                    if onode == node:                        cg = genome.connections[conn_key]                        inputs.append((inode, cg.weight))                ng = genome.nodes[node]                aggregation_function = config.genome_config.aggregation_function_defs.get(ng.aggregation)                activation_function = config.genome_config.activation_defs.get(ng.activation)                node_evals.append((node, activation_function, aggregation_function, ng.bias, ng.response, inputs))        return FeedForwardNetwork(config.genome_config.input_keys, config.genome_config.output_keys, node_evals)

Also I want to be able to mutate the neural network(connections, weights, ect).