<｜begin▁of▁sentence｜>### Instruction:
Imagine we have a new mothers' support network, outlined in a diagram with the connections between members represented as pathways ('A' to 'B', 'B' to 'C', and so on). Each pathway is an instance of support or interaction. In this network, we've observed that certain groups of new moms naturally form close-knit communities based on shared experiences and support needs—for example, we have one group comprised of mothers 'A', 'B', and 'C', and another of 'D', 'E', and 'F'.

Now, as a postpartum doula tasked with ensuring that the support structure is balanced and that each mother receives the attention she needs without being isolated or overwhelmed, it's important to examine if these two groups are self-contained. In other words, do the members of these groups interact exclusively with each other within their respective communities, effectively creating a harmonious split in our network where every mother is included in a supportive environment without overlap?

Can we use an organizational tool, akin to networkx in the digital realm, to confirm whether these groups of new mothers ['A', 'B', 'C'] and ['D', 'E', 'F'] form a complete and non-overlapping support system within our entire network, assuring that our community's structure is indeed nurturing and effective?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C'), ('A', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'F'), ('F', 'A')])

# Define communities
communities = [{'A', 'B', 'C'}, {'D', 'E', 'F'}]

# Check if the communities form a valid partition of G
is_valid_partition = nx.algorithms.community.quality.is_partition(G, communities)

print('Is valid partition:', is_valid_partition)
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