<｜begin▁of▁sentence｜>### Instruction:
As a neuroscience researcher delving into brain network analysis, imagine you're exploring two distinct brain networks, each represented as a Petersen graph. These graphs depict neural connections, with each vertex representing a brain region colored to signify its activity pattern. 

Now, suppose you have data from two brain imaging studies, each mapping the activity of specific brain regions. In these studies, the vertices are divided into two groups, with the first half colored differently from the second half, reflecting varying activation levels.

To deepen your understanding, you seek to identify and catalogue all possible transformations, called isomorphisms, between these brain networks while respecting the vertex colors. In simpler terms, you're tasked with finding ways to match up brain regions between the two networks, ensuring that regions with similar activity patterns are aligned. 

Your aim is to leverage network analysis tools like NetworkX, a Python library often used in neuroscience research, to systematically detect and document these color-preserving isomorphisms between the brain networks. This endeavor will provide valuable insights into how the brain's functional connectivity patterns align across different contexts, shedding light on neurological processes and potential implications for cognition and behavior.
### Response:
from igraph import Graph

# Assume we have two graphs defined with vertex colors
graph1 = Graph.Famous('petersen')
graph2 = Graph.Famous('petersen')

# Color vectors for the vertices (0 for first half, 1 for second half)
color1 = [0] * 5 + [1] * 5
color2 = [0] * 5 + [1] * 5

# Find all isomorphisms between graph1 and graph2 respecting the vertex colors
isomorphisms = graph1.get_isomorphisms_vf2(other=graph2, color1=color1, color2=color2)

# Print the isomorphisms
for iso in isomorphisms:
    print(iso)
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