What does this do intutively do wrt persistent Homology on images.

[prashkmr]

I was going thorugth the examples and am anot able to understand what this code does on the ouptu t reocsntucted images and contributes to the topoloss. Can the authors shed light on this code and how it is used in the topological loss calculation.

''' Diagramlayer Toplevel Setup'''
dtype=torch.float32
width, height = 28, 28
axis_x = np.arange(0, width)
axis_y = np.arange(0, height)
grid_axes = np.array(np.meshgrid(axis_x, axis_y))
grid_axes = np.transpose(grid_axes, (1, 2, 0))
from scipy.spatial import Delaunay
tri = Delaunay(grid_axes.reshape([-1, 2]))
faces = tri.simplices.copy()
F = DiagramlayerToplevel().init_filtration(faces)
diagramlayerToplevel = DiagramlayerToplevel.apply
''' '''

It is used with the generated image as :
top_loss = top_batch_cost(gen_image, diagramlayerToplevel, F)

How to interpret this code and the implementation/