code_samples/basic

##
<pre>
+from accelerate import Accelerator
+accelerator = Accelerator()
+dataloader, model, optimizer scheduler = accelerator.prepare(
+        dataloader, model, optimizer, scheduler
+)

for batch in dataloader:
    optimizer.zero_grad()
    inputs, targets = batch
-    inputs = inputs.to(device)
-    targets = targets.to(device)
    outputs = model(inputs)
    loss = loss_function(outputs, targets)
-    loss.backward()
+    accelerator.backward(loss)
    optimizer.step()
    scheduler.step()</pre>
##
Everything around `accelerate` occurs with the `Accelerator` class. To use it, first make an object.
Then call `.prepare` passing in the PyTorch objects that you would normally train with. This will
return the same objects, but they will be on the correct device and distributed if needed. Then
you can train as normal, but instead of calling `loss.backward()` you call `accelerator.backward(loss)`.
Also note that you don't need to call `model.to(device)` or `inputs.to(device)` anymore, as this
is done automatically by `accelerator.prepare()`.

##
To learn more checkout the related documentation:
- [Basic Tutorial](https://huggingface.co/docs/accelerate/basic_tutorials/migration)
- [The Accelerator](https://huggingface.co/docs/accelerate/package_reference/accelerator)