code: def test_basic(self) -> None:
      torch.manual_seed(0)
      B = 2
      D = 8
      dense_in_features = 100
      eb1_config = EmbeddingBagConfig(
          name="t1",
          embedding_dim=D,
          num_embeddings=100,
          feature_names=["f1", "f3"],
      )
      eb2_config = EmbeddingBagConfig(
          name="t2",
          embedding_dim=D,
          num_embeddings=100,
          feature_names=["f2"],
      )
      ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
      sparse_nn = DLRM_Transformer(
          embedding_bag_collection=ebc,
          dense_in_features=dense_in_features,
          dense_arch_layer_sizes=[20, D],
          over_arch_layer_sizes=[5, 1],
      )
      features = torch.rand((B, dense_in_features))
      sparse_features = KeyedJaggedTensor.from_offsets_sync(
          keys=["f1", "f3", "f2"],
          values=torch.tensor([1, 2, 4, 5, 4, 3, 2, 9, 1, 2, 3]),
          offsets=torch.tensor([0, 2, 4, 6, 8, 10, 11]),
      )
      logits = sparse_nn(
          dense_features=features,
          sparse_features=sparse_features,
      )
      self.assertEqual(logits.size(), (B, 1))
      expected_logits = torch.tensor([[-0.2593], [-0.1043]])
      self.assertTrue(
          torch.allclose(
              logits,
              expected_logits,
              rtol=1e-4,
              atol=1e-4,
          )
      )
  
imports: import unittest
  import torch
  from torchrec.datasets.utils import Batch
  from torchrec.models.dlrm import DLRMTrain
  from torchrec.models.experimental.transformerdlrm import DLRM_Transformer, InteractionTransformerArch
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 1 for calling the DLRMTransformerTest obj's underlying member method code with example usage: obj.test_basic() without return types