<｜begin▁of▁sentence｜>### Instruction:
Imagine you're editing a pair of images intended to have a subtle but intentional similarity in their patterns—think of it as ensuring they have a 'correlated' aesthetic. In the digital world of graph photography, instead of using your usual tools, you'll be working with the 'rdpg_corr' function from the editing suite graspologic to achieve this effect. 

Now, just as you might adjust the hue and saturation using a two-tone effect, you're tasked to create a set of two 'photo-graphs' that share a common underlying structure defined by a '2-dimensional Dirichlet distribution'. This will ensure that the connection patterns—akin to the details in your photographs—have a certain level of correlation. How would you go about composing this pair of images using the rdpg_corr function to craft this unique, intertwined graphical artwork?
### Response:
import numpy as np
from graspologic.simulations import rdpg_corr

np.random.seed(1234)
X = np.random.dirichlet([1, 1], size=5)
Y = None
G1, G2 = rdpg_corr(X, Y, 0.3, rescale=False, directed=False, loops=False)

print(G1)
print(G2)
<|EOT|>
