I need a clean, well-commented Jupyter Notebook that shows how latent-space vector arithmetic works inside a DCGAN, written in Python with PyTorch. The notebook should train (or load) a small DCGAN model, then walk through the classic tricks—adding, subtracting and smoothly interpolating latent vectors—to prove how semantic concepts emerge in the latent space. For more details regarding architecture and dataset, please refer below sheet. https://docs.google.com/spreadsheets/d/15IBceyNR-cKRLw6DSWAyyCedj8SEcnCXab_QbCU2TG4/edit?usp=drivesdk What I expect to see • Clearly structured code cells that: – prepare the data, – define and train/load the DCGAN in PyTorch, – perform vector arithmetic operations, and – visualise the results side-by-side for easy — Delievar last checkpoints for generator and discriminators. comparison. • Inline markdown explanations so a reader with basic GAN knowledge can follow the logic. • Reproducible results: set seeds and note any runtime choices so outputs are deterministic when possible. If the notebook runs end-to-end, produces illustrative image grids for each arithmetic operation, and keeps the runtime reasonable, I’ll consider the job complete.