code: def test_basic(self) -> None:
      torch.manual_seed(0)
      B = 4
      D = 3
      in_features = 10
      dense_arch = DenseArch(
          in_features=in_features, layer_sizes=[10, D]
      )
      dense_embedded = dense_arch(torch.rand((B, in_features)))
      self.assertEqual(dense_embedded.size(), (B, D))
  
      expected = torch.tensor(
          [
              [0.2351, 0.1578, 0.2784],
              [0.1579, 0.1012, 0.2660],
              [0.2459, 0.2379, 0.2749],
              [0.2582, 0.2178, 0.2860],
          ]
      )
      self.assertTrue(
          torch.allclose(
              dense_embedded,
              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 1 for calling the DenseArchTest obj's underlying member method code with example usage: obj.test_basic() without return types