Given the TreeNode structure built in Part 3:

root = TreeNode("A")
b, c, d, e, f = [TreeNode(x) for x in "BCDEF"]
root.add_child(b)
root.add_child(c)
root.add_child(d)
c.add_child(e)
c.add_child(f)

What are the expected outputs of preorder(root) and postorder(root) respectively?

Using the action history tree from Part 5, what are the values of match.value for:

match1 = find_node(history, "Delete line")
match2 = find_node(history, "Save file")

In Part 1 of the lab, you are given the following graph representation:

tree = {
    1: {2},
    2: {1, 3, 4},
    3: {2},
    4: {2, 5, 6},
    5: {4},
    6: {4}
}

What is the exact list returned by calling prufer_encode(tree)?

In the lab, you perform the following round-trip verification test:

seq = [2, 4, 4, 2]
decoded_tree = prufer_decode(seq)
print(prufer_encode(decoded_tree) == seq)

If both functions are implemented correctly, what is printed?

In the TODO instructions for prufer_encode(tree), why is step 1 instructed to copy the adjacency sets of the dictionary?

For values = [10, 20, 30, 40, 50, 60], what list does array_preorder(values) return?

(Format answer exactly as a Python list string, e.g. [1, 2, 3])

In prufer_decode(sequence), after processing all elements in the input sequence list, what final step completes the graph reconstruction?

In Part 4, you write the recursive inner function visit(i) for array_preorder(values):

def array_preorder(values):
    result = []
    def visit(i):
        # TODO
        pass
    if values:
        visit(0)
    return result

Which recursive call pattern correctly fills result in preorder for an array representation?

In Part 3, you construct a binary tree using BinaryNode:

binary_root = BinaryNode(
    8,
    BinaryNode(3, BinaryNode(1), BinaryNode(6)),
    BinaryNode(10, None, BinaryNode(14))
)

What list does inorder(binary_root) produce?

In Part 5, you implement find_node(node, target) to perform Depth-First Search on an action history tree:

history = TreeNode("Start")
type_text = TreeNode("Type text")
open_file = TreeNode("Open file")
add_title = TreeNode("Add title")
delete_line = TreeNode("Delete line")
history.add_child(type_text)
history.add_child(open_file)
type_text.add_child(add_title)
type_text.add_child(delete_line)

Which Python snippet correctly implements DFS to locate a target node?