<｜begin▁of▁sentence｜>### Instruction:
Given a graph stored in graph_data.gml, which may contain attributes like 'attr1', 'attr2' for graphs, 'v_attr1', 'v_attr2' for vertices and 'e_attr1', 'e_attr2' for edges, how would you convert this igraph object to a graph-tool object while maintaining selected attributes, and then calculate the average clustering coefficient using networkx?
### Response:
from igraph import Graph
import igraph as ig

# Read a graph from a file
igraph_g = ig.read('graph_data.gml')

# Define which attributes the user would like to transfer
graph_attributes = {'attr1': 'string', 'attr2': 'int'}
vertex_attributes = {'v_attr1': 'string', 'v_attr2': 'int'}
edge_attributes = {'e_attr1': 'string', 'e_attr2': 'int'}

# Convert the igraph object to a graph-tool object
graph_tool_g = igraph_g.to_graph_tool(graph_attributes=graph_attributes,
                                       vertex_attributes=vertex_attributes,
                                       edge_attributes=edge_attributes)

# Later on, if you want to perform a task with networkx that is not available in igraph or graph-tool
import networkx as nx
# For example, calculating the average clustering coefficient
gx = nx.read_gml('graph_data.gml')
avg_clustering = nx.average_clustering(ngx)
print('Average clustering coefficient:', avg_clustering)
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