What are learning rate schedulers in PyTorch and how do you use them?

import torch, torch.optim as optim
import torch.nn as nn

model = nn.Linear(128, 10)
optimizer = optim.SGD(model.parameters(), lr=0.1)

# ── StepLR: multiply lr by 0.1 every 30 epochs
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)

# ── CosineAnnealingLR: smooth cosine decay
scheduler = optim.lr_scheduler.CosineAnnealingLR(
    optimizer, T_max=100, eta_min=1e-6
)

# ── OneCycleLR: requires total_steps at init
scheduler = optim.lr_scheduler.OneCycleLR(
    optimizer,
    max_lr=0.1,
    total_steps=100 * len([1]*1000),  # epochs * batches_per_epoch
)

# ── ReduceLROnPlateau: triggered by validation loss
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
    optimizer, mode="min", factor=0.5, patience=5, verbose=True
)

# ── Training loop integration
for epoch in range(100):
    train_loss = 0.0  # ... train ...
    val_loss   = 0.0  # ... validate ...

    # Most schedulers: step after epoch
    scheduler.step()              # for StepLR, CosineAnnealingLR etc.
    # scheduler.step(val_loss)    # for ReduceLROnPlateau (needs metric)

    # Check current lr
    current_lr = optimizer.param_groups[0]["lr"]
    print(f"Epoch {epoch}: lr={current_lr:.6f}")