How would you debug and fix a Colab notebook that trains a denoising neural network on MNIST so that (a) the training loss steadily decreases and (b) the evaluation loss is close to the training loss? Specifically: ( 1) Data: detect and correct a train/test distribution mismatch so the test set covers all digits 0–9, and ensure identical normalization statistics are applied to training and test data; ( 2) Model: remove or replace an inappropriate final ReLU so the output range supports negative values typical of the denoised signal; ( 3) Optimization: add the missing optimizer.zero_grad() and put backward() and optimizer.step() in the correct order. Describe the sanity checks, assertions, metrics, and minimal code changes you would use to validate each fix, and show the key code snippets.