<｜begin▁of▁sentence｜>### Instruction:
In the context of product network analysis, imagine we're evaluating the connectivity of different elements within a modular design system. We have a model representing the system as an undirected, weighted interaction map with four components, labeled 0 through 3. The connections between these components are defined as follows: component 0 is linked to component 1 with a relationship strength of 1, to component 2 with the same strength of 1; component 1 also connects to component 2 and to component 3, both with a strength of 1.

Moreover, our current design iteration has grouped these components into two functional clusters for better synergy. The grouping arrangement is such that components 0 and 1 belong to cluster 0, while components 2 and 3 are in cluster 1.

As an integral part of design optimization, we're interested in evaluating the effectiveness of this clustering. Specifically, we want to measure the modularity of each cluster to understand how densely connected the components within each cluster are, in contrast to components outside the cluster. Calculating the modularity will give us insights into how well we've managed to create a cohesive modular design that promotes efficient interactions within clusters while minimizing extraneous connections between them.

In technical terms, for our pursuit of design excellence and optimization, could you elaborate on how we could compute the modularity components for each cluster using the NetworkX API, ensuring we take into account the weighted nature of the interactions between the system's components?
### Response:
import networkx as nx
from graspologic.partition import modularity_components

# Create an undirected graph
G = nx.Graph()
G.add_edges_from([(0, 1, {'weight': 1}), (0, 2, {'weight': 1}), (1, 2, {'weight': 1}), (1, 3, {'weight': 1})])

# Define a partition dictionary where each node is assigned to a community
partition = {0: 0, 1: 0, 2: 1, 3: 1}

# Calculate the modularity components for each community
modularity = modularity_components(G, partition, weight_attribute='weight')
print(modularity)
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