code: def test_single_step_sparse_arch_synthetic_reward(self):
      state_dense_dim = 10
      action_dense_dim = 2
      dense_sizes = [256, 32]
      dense_activations = ["sigmoid", "relu"]
      overall_sizes = [128, 1]
      overall_activations = ["sigmoid", "relu"]
      # Fake embedding bag configs
      embedding_table_size = 1000
      embedding_dim = 32
      num_sparse_features = (
          2  # refer to watched_ids and liked_ids below
      )
      embedding_bag_configs = [
          EmbeddingBagConfig(
              name="video_id",
              feature_names=["watched_ids", "liked_ids"],
              num_embeddings=embedding_table_size,
              embedding_dim=embedding_dim,
          )
      ]
      embedding_bag_col = EmbeddingBagCollection(
          device=torch.device("meta"), tables=embedding_bag_configs
      )
      reward_net = SyntheticRewardSparseArchNet(
          SingleStepSyntheticSparseArchRewardNet(
              state_dense_dim=state_dense_dim,
              action_dense_dim=action_dense_dim,
              dense_sizes=dense_sizes,
              dense_activations=dense_activations,
              overall_sizes=overall_sizes,
              overall_activations=overall_activations,
              embedding_bag_collection=embedding_bag_col,
          )
      )
      net = reward_net.export_mlp()
      assert net.state_dense_arch[0].in_features == state_dense_dim
      assert net.state_dense_arch[0].out_features == dense_sizes[0]
      assert net.state_dense_arch[2].in_features == dense_sizes[0]
      assert net.state_dense_arch[2].out_features == dense_sizes[1]
      assert net.action_dense_arch[0].in_features == action_dense_dim
      assert net.action_dense_arch[0].out_features == dense_sizes[0]
      assert net.action_dense_arch[2].in_features == dense_sizes[0]
      assert net.action_dense_arch[2].out_features == dense_sizes[1]
      assert (
          net.sparse_arch.embedding_bag_collection == embedding_bag_col
      )
      # the dim of the input to overall arch is 2D + 2F + F choose 2
      # See the explanation in SingleStepSyntheticSparseArchRewardNet
      assert (
          net.overall_arch[0].in_features
          == 2 * dense_sizes[1]
          + 2 * num_sparse_features
          + num_sparse_features * (num_sparse_features - 1) / 2
      )
      assert net.overall_arch[0].out_features == overall_sizes[0]
      assert net.overall_arch[2].in_features == overall_sizes[0]
      assert net.overall_arch[2].out_features == overall_sizes[1]
  
file_path: ReAgent/reagent/test/models/test_synthetic_reward_net.py
imports: import logging
  import unittest
  import torch
  from reagent.core import parameters
  from reagent.models.synthetic_reward import _gen_mask, NGramConvolutionalNetwork, NGramFullyConnectedNetwork, SequenceSyntheticRewardNet, SingleStepSyntheticRewardNet, SyntheticRewardNet, TransformerSyntheticRewardNet
  from reagent.models.synthetic_reward_sparse_arch import SingleStepSyntheticSparseArchRewardNet, SyntheticRewardSparseArchNet
  from torchrec import EmbeddingBagCollection, EmbeddingBagConfig
label_desc: Use image node_id 6 for calling the TestSyntheticReward obj's underlying member method code with example usage: obj.test_single_step_sparse_arch_synthetic_reward() without return types