```python
from torchrec import DataLoader
from torchrec.dlrm import DLRM
from torchrec.optim import Optimizer
from torchrec.utils import initialize_process_group, get_device, get_rank, get_backend, enable_optimizer_fusion, distribute_model, overlap_communication, overlap_compute, overlap_device_transfer

def _get_random_dataloader(num_embeddings, batch_size, pin_memory):
    # Code for _get_random_dataloader function
    pass

def train(num_embeddings, embedding_dim, dense_architecture, over_architecture, learning_rate):
    initialize_process_group()
    device = get_device()
    rank = get_rank()
    backend = get_backend()
    
    model = DLRM(num_embeddings, embedding_dim, dense_architecture, over_architecture)
    enable_optimizer_fusion()
    distribute_model(model)
    overlap_communication()
    overlap_compute()
    overlap_device_transfer()
    
    optimizer = Optimizer(learning_rate)
    training_iterator = DataLoader()
    
    # Code for training the model using training_iterator
    
if __name__ == "__main__":
    train(num_embeddings=100, embedding_dim=10, dense_architecture=[50, 20], over_architecture=[30, 10], learning_rate=0.001)
```