<｜begin▁of▁sentence｜>### Instruction:
As an app developer, you're tasked with developing an engaging feature for a mobile game that involves creating complex puzzles for players to solve. To achieve this, you've decided to incorporate a unique puzzle network within the game's design based on a Margulis-Gabber-Galil (MGG) graph structure.

To create the puzzle network, you'll need to utilize the NetworkX library to generate a non-simple Margulis-Gabber-Galil undirected MultiGraph with n^2 nodes. This graph should allow multiple edges between nodes (multiedges) and self-loops to add complexity to the puzzle, providing a richer experience for your players.

For one of the game's levels, you've chosen to design the puzzle around a network consisting of 3^2 nodes. You wish to know the specific details of the puzzle network, particularly the total number of nodes and edges this MGG graph would contain, so you can effectively calibrate the level's difficulty.

Could you provide some insight into how you'd approach generating this MGG graph using NetworkX, adhering to the game's need for it to be a non-simple, undirected multi-edged network? Also, include the details of the node and edge count for a 3x3 node configuration, which will help in the planning of the game's level design.
### Response:
import networkx as nx

# Generate a Margulis-Gabber-Galil graph on n^2 nodes
def create_margulis_gabber_galil_graph(n):
    try:
        G = nx.margulis_gabber_galil_graph(n)
        return G
    except nx.NetworkXError as e:
        print(e)

# Example usage
n = 3
G = create_margulis_gabber_galil_graph(n)
print(f'The graph has {G.number_of_nodes()} nodes and {G.number_of_edges()} edges.')
<|EOT|>
