 Here is the Python code based on your instructions:

```python
import torch
import torchrec

def _get_random_dataloader(num_embeddings, batch_size, pin_memory):
    return torch.utils.data.DataLoader(
        torchrec.test_dataset_train_val_and_test_sets(),
        batch_size=batch_size,
        num_workers=4,
        collate_fn=torchrec.gen_model_and_input,
        pin_memory=pin_memory
    )

def train(num_embeddings, embedding_dim, dense_arch_layer_sizes, over_arch_layer_sizes, learning_rate, batch_size, pin_memory):
    torchrec.seed_and_log()
    torch.manual_seed(0)

    device, rank, backend = torchrec.initialize_and_test_parameters()

    model = torchrec.create_module_and_freeze(
        num_embeddings, embedding_dim, dense_arch_layer_sizes, over_arch_layer_sizes
    )

    model = torchrec.convert_optimizer_type_and_kwargs(model, 'adam', {'lr': learning_rate})

    model = torchrec._setup_cpu_model_and_opt(model, device, rank, backend)

    model = torchrec.overlap_communication(model)
    model = torchrec.compute_and_output_dist(model)

    optimizer = model.optimizer

    train_loader = _get_random_dataloader(num_embeddings, batch_size, pin_memory)

    for epoch in range(10):
        for batch_idx, (inputs, labels) in enumerate(train_loader):
            optimizer.zero_grad()

            with torch.set_grad_enabled(torchrec.should_compute(rank, batch_idx, len(train_loader))):
                outputs = model(*inputs)
                loss = torch.nn.functional.cross_entropy(outputs, labels)
                loss.backward()
                optimizer.step()

                torchrec._update_and_evict(model, device, rank)

if __name__ == "__main__":
    train(
        10,
        16,
        [64, 32, 16],
        [64, 32, 16],
        0.001,
        64,
        True
    )
```

Please note that this code assumes the presence of the `torchrec` library and the dataset is defined in `torchrec.test_dataset_train_val_and_test_sets()`. You may need to adjust the code according to your specific dataset and library structure.