<｜begin▁of▁sentence｜>### Instruction:
As a Clinical Dietitian, you're often tasked with tailoring nutrition plans to meet the specific needs of individuals with various medical conditions. Imagine you're working with a diverse group of patients, each requiring a unique dietary approach. In this scenario, think of the patients as nodes in a graph, with their medical conditions represented as attributes like node colors or shapes. Now, consider the need to visualize these dietary plans in a way that goes beyond standard methods.

Suppose you're using a tool like the AbstractCairoDrawer in Python, which is akin to a specialized kitchen appliance designed to create custom recipes. Your challenge is to devise a method to draw these dietary plans onto the graph, incorporating specific visual properties like node colors or edge shapes that aren't readily available through default methods.

To achieve this, you'll need to implement a custom drawing method within the AbstractCairoDrawer framework. This method acts as your recipe, detailing how each node's unique dietary requirements are visualized, ensuring that the semantics of the graph, akin to the patients' conditions, remain unchanged while reflecting the tailored dietary plans effectively.
### Response:
import igraph as ig
import cairo

class CustomDrawer(ig.drawing.AbstractCairoDrawer):
    def draw(self, context):
        # Implement custom drawing logic
        # Example: draw a red circle
        context.set_source_rgb(1, 0, 0)
        context.arc(100, 100, 50, 0, 2 * 3.14159)
        context.fill()

# Create a graph
g = ig.Graph.Tree(3, 2)

# Create a drawing surface
surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, 400, 400)
ctx = cairo.Context(surface)

# Use custom drawer to draw on the surface
custom_drawer = CustomDrawer(g, bbox=(400, 400), vertex_size=20)
custom_drawer.draw(ctx)

# Save to a file
surface.write_to_png('custom_tree.png')
<|EOT|>
