<｜begin▁of▁sentence｜>### Instruction:
Given a graph with nodes A, B, C, D, E, and F, where nodes A, B, C, and D have a 'color' attribute set to 'Red', and nodes E and F have a 'color' attribute set to 'Blue', and edges ('A', 'E') and ('B', 'F') with an 'type' attribute set to 'Strong', and edges ('C', 'E') and ('D', 'F') with a 'type' attribute set to 'Weak', can you use the SNAP aggregation method provided by NetworkX to group these nodes and edges into a summary graph based on their 'color' and 'type' attributes?
### Response:
import networkx as nx

# Define the existing graph
nodes = {
    "A": dict(color="Red"),
    "B": dict(color="Red"),
    "C": dict(color="Red"),
    "D": dict(color="Red"),
    "E": dict(color="Blue"),
    "F": dict(color="Blue"),
}
edges = [
    ("A", "E", "Strong"),
    ("B", "F", "Strong"),
    ("C", "E", "Weak"),
    ("D", "F", "Weak"),
]

# Create a new graph and add the nodes and edges
G = nx.Graph()
for node in nodes:
    attributes = nodes[node]
    G.add_node(node, **attributes)

for source, target, edge_type in edges:
    G.add_edge(source, target, type=edge_type)

# Define the attributes that will be used to group the nodes and edges
node_attributes = ('color',)
edge_attributes = ('type',)

# Perform SNAP aggregation
summary_graph = nx.snap_aggregation(G, node_attributes=node_attributes, edge_attributes=edge_attributes)

# Print the nodes of the summary graph
for node, data in summary_graph.nodes(data=True):
    print(f'{node}: {data}')

# Print the edges of the summary graph
for edge, data in summary_graph.edges(data=True):
    print(f'{edge}: {data}')
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