Refactor(examples/mnist): Clarify loss reduction and correct dataset length

[sanshi0030]

This commit addresses two potential points of confusion and error in the MNIST example, as detailed in issue #623.

1.Explicit Loss Reduction: The train() function's use of F.nll_loss implicitly defaults to reduction='mean', whereas the test() function uses reduction='sum'.

This change makes the reduction='mean' explicit in the train() function. This improves code clarity.

2. Correct Dataset Size with Samplers: Using len(loader.dataset) to get the number of samples is incorrect when a Sampler (e.g., SubsetRandomSampler for a validation split) is used. It incorrectly reports the full dataset size, not the subset size.

The logic is updated to first check len(loader.sampler). If a sampler exists, its length is used. Otherwise, it falls back to len(loader.dataset). This ensures the correct number of samples is used for logging and calculations.

Fixes #623

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