code: def test_basic(self) -> None:
      B = 2
      D = 8
      dense_in_features = 100
  
      eb1_config = EmbeddingBagConfig(
          name="t2",
          embedding_dim=D,
          num_embeddings=100,
          feature_names=["f2"],
      )
  
      ebc = EmbeddingBagCollection(tables=[eb1_config])
      dlrm_module = DLRM_Projection(
          embedding_bag_collection=ebc,
          dense_in_features=dense_in_features,
          dense_arch_layer_sizes=[20, D],
          over_arch_layer_sizes=[5, 1],
          interaction_branch1_layer_sizes=[80, 40],
          interaction_branch2_layer_sizes=[80, 48],
      )
      dlrm = DLRMTrain(dlrm_module)
  
      features = torch.rand((B, dense_in_features))
      sparse_features = KeyedJaggedTensor.from_offsets_sync(
          keys=["f2"],
          values=torch.tensor(range(3)),
          offsets=torch.tensor([0, 2, 3]),
      )
      batch = Batch(
          dense_features=features,
          sparse_features=sparse_features,
          labels=torch.randint(2, (B,)),
      )
  
      _, (_, logits, _) = dlrm(batch)
      self.assertEqual(logits.size(), (B,))
  
imports: import unittest
  import torch
  from torch import nn
  from torch.testing import FileCheck
  from torchrec.datasets.utils import Batch
  from torchrec.fx import symbolic_trace
  from torchrec.models.dlrm import choose, DenseArch, DLRM, DLRM_DCN, DLRM_Projection, DLRMTrain, InteractionArch, InteractionDCNArch, InteractionProjectionArch, SparseArch
  from torchrec.modules.crossnet import LowRankCrossNet
  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 DLRM_ProjectionTrainTest obj's underlying member method code with example usage: obj.test_basic() without return types