code: def test_correctness(self) -> None:
      D = 4
      B = 3
      keys = [
          "f1",
          "f2",
          "f3",
          "f4",
      ]
      F = len(keys)
      F1 = 5
      F2 = 5
      I1 = nn.Identity()
      I2 = nn.Identity()
      inter_arch = InteractionProjectionArch(
          num_sparse_features=F,
          interaction_branch1=I1,
          interaction_branch2=I2,
      )
      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 (D + F1 * F2)
      self.assertEqual(concat_dense.size(), (B, D + F1 * F2))
  
      expected = torch.tensor(
          [
              [
                  0.4963,
                  0.7682,
                  0.0885,
                  0.1320,
                  0.5057,
                  0.6874,
                  0.5756,
                  0.8082,
                  0.6656,
                  0.5402,
                  0.3672,
                  0.5765,
                  0.8837,
                  0.2710,
                  0.7540,
                  0.9349,
                  0.7424,
                  1.0666,
                  0.7297,
                  1.3209,
                  1.2713,
                  1.2888,
                  1.9248,
                  0.9367,
                  0.4865,
                  0.7688,
                  0.9932,
                  1.3475,
                  0.8313,
              ],
              [
                  0.3074,
                  0.6341,
                  0.4901,
                  0.8964,
                  1.0706,
                  0.8757,
                  1.0621,
                  1.3669,
                  0.6122,
                  0.9342,
                  0.7316,
                  0.7294,
                  1.0603,
                  0.3866,
                  0.2011,
                  0.2153,
                  0.3768,
                  0.3638,
                  0.2154,
                  1.0712,
                  0.8293,
                  1.3000,
                  1.4564,
                  0.8369,
                  0.3655,
                  0.4440,
                  0.6148,
                  1.0776,
                  0.5871,
              ],
              [
                  0.4556,
                  0.6323,
                  0.3489,
                  0.4017,
                  0.7294,
                  1.3899,
                  0.9493,
                  0.6186,
                  0.7565,
                  0.9535,
                  1.5688,
                  0.8992,
                  0.7077,
                  1.0088,
                  1.1206,
                  1.9778,
                  1.1639,
                  0.8642,
                  1.1966,
                  1.1827,
                  1.8592,
                  1.3003,
                  0.9441,
                  1.1177,
                  0.4730,
                  0.7631,
                  0.4304,
                  0.3937,
                  0.3230,
              ],
          ]
      )
  
      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 InteractionProjectionArchTest obj's underlying member method code with example usage: obj.test_correctness() without return types