code: def create(genome, config):
          
  
          # Gather expressed connections.
          connections = [cg.key for cg in itervalues(genome.connections) 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 = []
                  node_expr = [] # currently unused
                  for conn_key in connections:
                      inode, onode = conn_key
                      if onode == node:
                          cg = genome.connections[conn_key]
                          inputs.append((inode, cg.weight))
                          node_expr.append("v[{}] * {:.7e}".format(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)
docs: Receives a genome and returns its phenotype (a FeedForwardNetwork).