Describe your change:

Added the KD-Tree construction algorithm to the repository.
The 'build_kdtree' function has been implemented to construct a KD-Tree from a list of points, handling different depths and dimensions.
Includes type annotations for better code clarity and maintenance.
The algorithm recursively splits the points based on the median of the axis determined by the depth of the tree.
Added doctests to verify the correctness of the algorithm.

Removed depth from test_build_kdtree: If depth is not used, it's best to remove it. The function build_kdtree should not rely on it unless explicitly designed to do so.
Check for KDNode: Simplified the type checking for KDNode to ensure the KD-Tree is built correctly.
Edge Case Handling: Ensured that edge cases are properly handled with the assumption that an empty KD-Tree should be handled by nearest_neighbour_search.
Ensure that your 'build_kdtree', 'hypercube_points', and 'nearest_neighbour_search' functions are correctly implemented and compatible with the tests.

• Add an algorithm?

Checklist:

• [+ ] I have read CONTRIBUTING.md.
• [+ ] This pull request is all my own work -- I have not plagiarized.
• [ +] I know that pull requests will not be merged if they fail the automated tests.
• [+ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• [+ ] All new Python files are placed inside an existing directory.
• [+ ] All filenames are in all lowercase characters with no spaces or dashes.
• [+ ] All functions and variable names follow Python naming conventions.
• [ +] All function parameters and return values are annotated with Python type hints.
• [ +] All functions have doctests that pass the automated testing.
• [+ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".