<｜begin▁of▁sentence｜>### Instruction:
As an animal behaviorist, imagine analyzing the movement patterns of a group of interconnected animals within their habitat. Each movement between locations is represented by a direct path, much like the edges in a network graph: these movements have been recorded as follows: traversals from den (0) to watering hole (1), from watering hole (1) to foraging area (2), from foraging area (2) to lookout point (3), from lookout point (3) back to den (0), den (0) to nesting site (4), from nesting site (4) to feeding ground (5), from feeding ground (5) to the social gathering spot (6), and lastly from the social gathering spot (6) back to nesting site (7), then to nesting site (4). 

In order to ensure the most efficient path for an animal tagged number 2 moving to rendezvous with its peer tagged number 6, how might one employ the concept of 'bidirectional_shortest_path' akin to our network analysis tools to facilitate the comprehension of such an optimal route within this web of interconnectivity? The goal would be to apply our understanding of this method without altering the essence of the scenario you initially proposed.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add edges to the graph
G.add_edges_from([(0,1), (1,2), (2,3), (3,0), (0,4), (4,5), (5,6), (6,7), (7,4)])

# Find the shortest bidirectional path between two nodes
path = nx.bidirectional_shortest_path(G, 2, 6)

# Output the path
print('Shortest bidirectional path from node 2 to 6:', path)
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