<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working with a group of individuals, each striving to support one another in their rehabilitation journey. Picture these individuals as points in a network, where each point is connected to another by a directed path of aid and collaboration. The connections in this network are like a series of roads where assistance can travel in both directions.

In this scenario, consider a specific pattern of interactions: Person A assists Person B, B also helps A, B supports C, C aids B, and D extends help to A. We're interested in understanding the degree to which these acts of support are mutual, with each individual reciprocating the help they receive.

Your task is to examine this network of support and determine the proportion of these interactions that are two-way streets – that is, for every act of assistance given, an equivalent act is received in return. This measure, known as the network's overall reciprocity, will help us understand the balance of give and take among the individuals in our group.

There's no need to look for a GML file here, as the data is already provided in the form of the connections outlined above. What we seek is the essence of mutual support encapsulated in a simple ratio, reflecting the community spirit and collaborative ethos of your group's rehabilitation efforts.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([('A', 'B'), ('B', 'A'), ('B', 'C'), ('C', 'B'), ('D', 'A')])

# Compute overall reciprocity for the graph
reciprocity = nx.overall_reciprocity(G)

print('Overall reciprocity of the graph:', reciprocity)
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