<｜begin▁of▁sentence｜>### Instruction:
Hey lovely followers! 🌟 I've been brainstorming some cool connections for our latest social media strategy, and it's kind of like a game where we navigate from one hot topic to the next. Just imagine: we've got trending hashtags as our destinations, like #A is all about wellness, #B is beauty tips, and #C is about superfoods. Our journey has some rules, though – think of it as the cost in engagement effort to hop from one trend to the next. For instance, it takes 4 units of effort to connect wellness to beauty, 1 to link beauty to superfoods, a whole 5 to circle back to wellness from superfoods, and 8 if we want to go directly from wellness to superfoods. 🔄

Now here's where it gets super interesting! We've been flowing through these connections with some serious strategy, like putting 3 units of our effort into the wellness to beauty trend and 4 for the wellness to superfoods vibe. And from beauty, we dished out another 3 units to slide into those superfood tips. All that effort got recycled back into wellness from superfoods with a 4 unit push!

Think of it as a behind-the-scenes peek at what it takes to keep our feed fresh and engaging. I'm super curious to know the total 'effort score' from this strategy, and I've got this amazing tool called NetworkX that can do the math for us.

Can any of you data wizards out there help me tally up the total cost of our social flow using NetworkX's 'cost_of_flow' magic? It's like giving us the insider knowledge on just how much energy we're channeling to keep you all in the loop with the coolest trends! Drop a comment below if you can figure it out or share a trick on how to do it! ✨🔍 #SocialStrategyGame #EngagementEnergy #NetworkXNumbers
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges with weights (representing costs)
G.add_edge('A', 'B', weight=4)
G.add_edge('B', 'C', weight=1)
G.add_edge('C', 'A', weight=5)
G.add_edge('A', 'C', weight=8)

# Define a flow dictionary that respects the capacities
flowDict = {'A': {'B': 3, 'C': 4}, 'B': {'C': 3}, 'C': {'A': 4}}

# Calculate the cost of the flow
flow_cost = nx.cost_of_flow(G, flowDict)

print('The cost of the flow:', flow_cost)
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