 Here is the Python code based on your instructions:

```python
import torch
import torchrec
import torch.distributed as dist
import torch.multiprocessing as mp

def _get_random_dataloader(num_embeddings, batch_size, pin_memory):
    # Assuming you have a function to create your dataloader
    # Replace `create_dataloader` with your actual function
    return torchrec.data.create_dataloader(num_embeddings, batch_size, pin_memory)

def train(num_embeddings, embedding_dim, dense_arch_sizes, over_arch_sizes, learning_rate):
    # Initialize the process group
    dist.init_process_group(backend='nccl')
    rank = dist.get_rank()
    world_size = dist.get_world_size()

    # Set device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # Create DLRM model
    model = torchrec.dlrm.DLRM(
        num_embeddings=num_embeddings,
        embedding_dim=embedding_dim,
        dense_arch_sizes=dense_arch_sizes,
        over_arch_sizes=over_arch_sizes,
        num_classes=1,
        device=device
    )

    # Enable optimizer fusion
    model = torchrec.optim.fuse_optimizer(model, optimizer=torch.optim.Adam(model.parameters(), lr=learning_rate))

    # Distribute the model across devices
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[rank])

    # Overlap communication, compute, and device transfer during training
    model.train(overlap_backward=True, device_sync_period=1)

    # Training loop
    train_loader = _get_random_dataloader(num_embeddings, batch_size, pin_memory=True)
    for epoch in range(10):  # Adjust the number of epochs as needed
        for batch in train_loader:
            outputs = model(batch)
            loss = outputs.loss
            loss.backward()
            model.step()
            model.zero_grad()

if __name__ == "__main__":
    num_embeddings = 10
    embedding_dim = 16
    dense_arch_sizes = [128, 64]
    over_arch_sizes = [64]
    learning_rate = 0.001

    train(num_embeddings, embedding_dim, dense_arch_sizes, over_arch_sizes, learning_rate)
```

This code assumes that you have a function `create_dataloader` to create your dataloader. You should replace it with your actual function. Also, the training loop is a simple example, and you may need to adjust it according to your specific requirements.