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Decorative code sample: a short PyTorch training loop in train.py.

import torch
from torch import nn

# tiny classifier, illustrative only
model = nn.Sequential(nn.Linear(64, 128), nn.GELU(), nn.Linear(128, 10))
opt = torch.optim.AdamW(model.parameters(), lr=3e-4)

for epoch in range(50):
    for x, y in loader:
        loss = nn.functional.cross_entropy(model(x), y)
        loss.backward()
        opt.step()
        opt.zero_grad()
    print(f"epoch {epoch}  loss={loss.item():.4f}")
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