Fix OutOfMemoryError

✅ Solution

OutOfMemoryError in PyTorch Lightning typically occurs when the GPU runs out of memory during training. Fix this by reducing the `batch_size` in your DataLoader, using gradient accumulation with `accumulate_grad_batches` in the Trainer, or offloading computations to the CPU using `torch.cuda.empty_cache()` periodically and enabling `model.half()` for mixed precision training if applicable. Consider larger GPUs or distributed training for further memory relief.