<｜begin▁of▁sentence｜>### Instruction:
Suppose we're in the midst of designing a novel diagnostic tool that relies on the principles of chemical data networks. We're modeling our data network after a hypercube structure due to its symmetry and unique properties, akin to the way materials crystallize in different dimensional structures. Now, for this network to function optimally in analyzing chemical data, it's pivotal that each node (representing a chemical compound or process node) maintains uniform communication paths, ensuring consistent reaction times across different stages of the diagnostic process.

In this context, we're utilizing a hypercube graph of dimension 6 as our template. To ascertain whether this graph maintains equal interaction paths, much like ensuring consistent pathways in a material or chemical synthesis process, we should determine if the hypercube is 'distance regular.' Could you utilize the NetworkX function `is_distance_regular` to confirm this structural property for our diagnostic tool’s data network model? This verification will be critical in ensuring that our tool's network reacts uniformly, facilitating regularity in analysis much like how a well-designed chemical process assures product consistency.
### Response:
import networkx as nx

# Create a hypercube graph of dimension 6
G = nx.hypercube_graph(6)

# Check if the graph is distance regular
is_dist_reg = nx.is_distance_regular(G)

print('Is the hypercube graph distance regular?', is_dist_reg)
<|EOT|>
