Understanding the FCI outputs (graph vs. printed edges)

I apologize if this is a naive question, but I want to ask. In my PAG resulting from FCI, I see that X4 --> X5 relationship is observed in the graph. But, the printed relationships between edges and nodes as a result of running FCI algorithm, does not include the same path that is in the PAG graph. How should I interpret this? Thank you.

from causallearn.search.ConstraintBased.FCI import fci
from causallearn.utils.GraphUtils import GraphUtils
g, edges = fci(df.to_numpy())
pdy = GraphUtils.to_pydot(g )

Edit: Updated PAG, g.graph, and edges. This time FCI outputs X1 --> X5 and X2 --> X3. But in the PAG, we can see X4 --> X5, X2 --> X5, X6 --> X5, etc. as well.

g.graph:

edges:

Start with the FCI call in the issue and the GraphUtils.to_pydot(g) entry point, then inspect how the returned graph and edges values are represented. Compare the PAG rendering with the printed edge collection and document the distinction and how users should interpret each output.