Added Code for Gradient Accumulation to work for basic_training (#8961)

added line allowing gradient accumulation to work for basic_training example

docs/source/en/tutorials/basic_training.md

@@ -340,6 +340,7 @@ Now you can wrap all these components together in a training loop with 🤗 Acce
 ...                 loss = F.mse_loss(noise_pred, noise)
 ...                 accelerator.backward(loss)
 
+...             if (step + 1) % config.gradient_accumulation_steps == 0:
 ...                 accelerator.clip_grad_norm_(model.parameters(), 1.0)
 ...                 optimizer.step()
 ...                 lr_scheduler.step()