<｜begin▁of▁sentence｜>### Instruction:
As an athletic trainer, you provide preventive care, treatment, and rehabilitation for athletes' injuries and conditions. In a practical scenario, imagine you are working with a team of athletes and you have graph data stored in a file named "graph_data.gml". This graph may contain attributes such as 'attr1' and 'attr2' for the entire graph, 'v_attr1' and 'v_attr2' for vertices, and 'e_attr1' and 'e_attr2' for edges. You need to convert this graph from an igraph object to a graph-tool object while preserving selected attributes. Additionally, you are tasked with calculating the average clustering coefficient using NetworkX. This task requires an understanding of both igraph and graph-tool libraries in Python, as well as the usage of the provided attributes for computation.
### 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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