Give me a sbd training split
Sure! Here's an example of a SBD (Single-Block Deep) training split for you:
```python
from transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModel.from_pretrained("sbd")

input_ids = torch.tensor(range(10000), dtype=torch.int64).unsqueeze(-1)  # Unsqueeze the last dimension to get batch size
labels = torch.tensor([0], dtype=torch.long).unsqueeze(-1) # Keep the first label as 0 and others as 1

train_data = torch.rand((1000, 32), device="cuda:0").to(model.device)  # Convert input data into a PyTorch tensor
test_data = torch.rand((100, 32), device="cuda:0").to(model.device)  # Convert test data into a PyTorch tensor


for i in range(1000):
    inputs = torch.cat((input_ids[i][0].tolist(), input_ids[i][1].tolist()), -1)
    outputs = model(inputs)[0]  # Run the model on each batch of inputs
    loss = torch.mean(torch.sum( outputs - labels, axis=-1)) / len(labels)  # Calculate loss from predicted output vs actual labels
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
```
In this example, we are using `AutoTokenizer` and `AutoModel` from Transformers to load a pre-trained GPT-2 model and generate input text pairs for our training set. We then use a custom version of the PyTorch library to convert the input