code: def test_cross_net_numercial_forward_1_expert(self) -> None:
      torch.manual_seed(0)
  
      batch_size = 3
      num_layers = 20
      in_features = 2
      input = torch.randn(batch_size, in_features)
  
      # test using vector for crossing
      dcn = LowRankMixtureCrossNet(
          in_features=in_features,
          num_layers=num_layers,
          num_experts=1,
          low_rank=10,
      )
      output = dcn(input)
      expected_output = torch.Tensor(
          [
              [3.9203, -0.2686],
              [-9.5767, 0.8621],
              [-2.5836, -1.8124],
          ]
      )
      self.assertTrue(
          torch.allclose(output, expected_output, rtol=1e-4, atol=1e-4)
      )
  
imports: import unittest
  import torch
  from torch.fx import GraphModule, Tracer
  from torchrec.modules.crossnet import CrossNet, LowRankCrossNet, LowRankMixtureCrossNet, VectorCrossNet
label_desc: Use image node_id 2 for calling the TestLowRankMixtureCrossNet obj's underlying member method code with example usage: obj.test_cross_net_numercial_forward_1_expert() without return types