<｜begin▁of▁sentence｜>### Instruction:
In the latest series of our tech-themed podcast, we're exploring the intricacies of digital networking models. In an upcoming episode, we'll dive deep into the world of directed graphs to understand how data flows and is interconnected. Our script includes a fascinating scenario in which we have a directed graph that represents a network of digital content creators and their collaborations. The connections, or "edges", between these creators are represented by pairs and weights, illustrating the strength of their collaborations.

Our dataset for this exclusive episode looks something like this: we have a network with four collaborations, illustrated by the following set - [('A', 'B', 2), ('B', 'C', 3), ('A', 'C', 1), ('C', 'A', 4)]. Each tuple represents a directed edge between two content creators and the weight of their collaboration—think of the weight as the amount of shared content or the frequency of their joint projects.

For a particular segment, we're focusing on two influential creators within this network, denoted as 'A' and 'B.' We're aiming to quantify the collective reach or impact of their collaborations, which in the world of graph theory, is referred to as the "volume" of this subset of nodes within the greater network. The challenge here is to compute the volume considering the weight of the connections as the measure of collaboration strength.

Listeners will be on the edge of their seats as we calculate this metric, which reflects the joint influence of these creators in the digital content landscape. Can your expertise in network analysis help us illustrate this concept vividly on the air?

As we approach the recording session, we'll reference our script and the dataset to ensure our narrative is both accurate and engaging. Get ready to tune in and crunch some numbers with us!
### Response:
import networkx as nx

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

# Add edges with weights
G.add_weighted_edges_from([('A', 'B', 2), ('B', 'C', 3), ('A', 'C', 1), ('C', 'A', 4)])

# Define the set of nodes for which we want to calculate the volume
S = ['A', 'B']

# Calculate the volume of the set of nodes
vol = nx.volume(G, S, weight='weight')

print('Volume of the set of nodes', S, 'is:', vol)
<|EOT|>
