code: def test_deepfm_with_lazy_shape(self) -> None:
      batch_size = 3
      output_dim = 30
      # the input embedding are in torch.Tensor of [batch_size, num_embeddings, embedding_dim]
      input_embeddings = [
          torch.randn(batch_size, 2, 64),
          torch.randn(batch_size, 2, 32),
          torch.randn(batch_size, 3, 100),
          torch.randn(batch_size, 5, 120),
      ]
      dense_module = torch.nn.Sequential(
          torch.nn.LazyLinear(output_dim),
          torch.nn.ReLU(),
      )
      deepfm = DeepFM(dense_module=dense_module)
  
      deep_fm_output = deepfm(input_embeddings)
  
      self.assertEqual(
          list(deep_fm_output.shape), [batch_size, output_dim]
      )
  
file_path: torchrec/torchrec/modules/tests/test_deepfm.py
imports: import unittest
  import torch
  from torchrec.fx import Tracer
  from torchrec.modules.deepfm import DeepFM, FactorizationMachine
label_desc: Use image node_id 2 for calling the TestDeepFM obj's underlying member method code with example usage: obj.test_deepfm_with_lazy_shape() without return types