code: def test_correctness(self) -> None:
      D = 4
      B = 3
      keys = [
          "f1",
          "f2",
          "f3",
          "f4",
      ]
      F = len(keys)
      DCN = nn.Identity()
      inter_arch = InteractionDCNArch(
          num_sparse_features=F,
          crossnet=DCN,
      )
      torch.manual_seed(0)
  
      dense_features = torch.rand((B, D))
      sparse_features = torch.rand((B, F, D))
  
      concat_dense = inter_arch(dense_features, sparse_features)
      #  B X (F*D + D)
      self.assertEqual(concat_dense.size(), (B, F * D + D))
  
      expected = torch.tensor(
          [
              [
                  0.4963,
                  0.7682,
                  0.0885,
                  0.1320,
                  0.0223,
                  0.1689,
                  0.2939,
                  0.5185,
                  0.6977,
                  0.8000,
                  0.1610,
                  0.2823,
                  0.6816,
                  0.9152,
                  0.3971,
                  0.8742,
                  0.4194,
                  0.5529,
                  0.9527,
                  0.0362,
              ],
              [
                  0.3074,
                  0.6341,
                  0.4901,
                  0.8964,
                  0.1852,
                  0.3734,
                  0.3051,
                  0.9320,
                  0.1759,
                  0.2698,
                  0.1507,
                  0.0317,
                  0.2081,
                  0.9298,
                  0.7231,
                  0.7423,
                  0.5263,
                  0.2437,
                  0.5846,
                  0.0332,
              ],
              [
                  0.4556,
                  0.6323,
                  0.3489,
                  0.4017,
                  0.1387,
                  0.2422,
                  0.8155,
                  0.7932,
                  0.2783,
                  0.4820,
                  0.8198,
                  0.9971,
                  0.6984,
                  0.5675,
                  0.8352,
                  0.2056,
                  0.5932,
                  0.1123,
                  0.1535,
                  0.2417,
              ],
          ]
      )
  
      self.assertTrue(
          torch.allclose(
              concat_dense,
              expected,
              rtol=1e-4,
              atol=1e-4,
          )
      )
  
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 3 for calling the InteractionDCNArchTest obj's underlying member method code with example usage: obj.test_correctness() without return types