<｜begin▁of▁sentence｜>### Instruction:
Imagine we're embarking on a creative endeavor to visualize the layout of a bustling film set, represented as a complex network of connected points within a 5x5 grid. Think of it as a detailed model for the intricate pathways between various departments, from lighting to sound, all crucial to a seamless production.

Now, let's delve into the heart of our network's resilience. I'd like to craft a compelling scenario where we identify every potential point, or node, where if removed, the continuity of our entire set would be disrupted. Essentially, we're seeking those critical junctures whose absence would partition our set into isolated clusters, hampering the smooth flow of our production.

To maintain accuracy, we'll extract these pivotal points while ensuring that the size of these groups of nodes, or cutsets, mirrors the very threshold of our set's connectivity – the minimum number of elements that must be removed to sever the network's seamless operations.

Integrate this detail into our script and ensure that it's represented with precision, aligning with the logistical artistry our film commands.
### Response:
import networkx as nx

# Create a grid graph
G = nx.grid_2d_graph(5, 5)

# Find all minimum node cuts
all_cuts = list(nx.all_node_cuts(G))

# Print the number of cutsets and check if all cutsets have cardinality equal to the node connectivity
num_cutsets = len(all_cuts)
each_cutset_size = all(len(cutset) == nx.node_connectivity(G) for cutset in all_cuts)

# Display results
print('Number of cutsets:', num_cutsets)
print('Each cutset has cardinality equal to node connectivity:', each_cutset_size)
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