<｜begin▁of▁sentence｜>### Instruction:
Imagine we're examining a complex biochemical network represented by a multigraph G, where the nodes symbolize various biochemical entities and the edges signify reactions or interactions between these entities. Some interactions are more prevalent or potent than others, which we've denoted with weights on the corresponding edges, while others are simply noted as connections without an assigned weight.

For our analysis, we need to create a sparse representation of this network's adjacency matrix using a SciPy CSR (Compressed Sparse Row) format. This matrix representation should accurately reflect the weighted nature of our network's interactions where applicable.

How would a biochemist, interested in preserving this crucial information about interaction strengths within the network, transition the data from the multigraph into a weighted adjacency matrix in CSR format? This process would aid in the further computational analysis of the biochemical pathways and their dynamics. Could you elucidate the method while maintaining the integrity of the weighted connections in our network?
### Response:
import networkx as nx
from scipy.sparse import csr_matrix

# Create a graph
G = nx.MultiDiGraph()
G.add_edge(0, 1, weight=2)
G.add_edge(1, 0)
G.add_edge(2, 2, weight=3)
G.add_edge(2, 2)

# Get the adjacency matrix in CSR format
S = nx.to_scipy_sparse_array(G, nodelist=[0, 1, 2], dtype=float, weight='weight', format='csr')

# Print the adjacency matrix
print(S.toarray())
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