code: def setUp(self) -> None:
      super().setUp()
  
      num_features = 4
      shared_features = 2
  
      initial_tables = [
          EmbeddingConfig(
              num_embeddings=(i + 1) * 11,
              embedding_dim=16,
              name="table_" + str(i),
              feature_names=["feature_" + str(i)],
          )
          for i in range(num_features)
      ]
  
      shared_features_tables = [
          EmbeddingConfig(
              num_embeddings=(i + 1) * 11,
              embedding_dim=16,
              name="table_" + str(i + num_features),
              feature_names=["feature_" + str(i)],
          )
          for i in range(shared_features)
      ]
  
      self.tables = initial_tables + shared_features_tables
      self.shared_features = [
          f"feature_{i}" for i in range(shared_features)
      ]
  
      self.embedding_groups = {
          "group_0": [
              f"{feature}@{table.name}"
              if feature in self.shared_features
              else feature
              for table in self.tables
              for feature in table.feature_names
          ]
      }
  
imports: import unittest
  from typing import Any, Dict, List, Optional, Tuple, Type
  import hypothesis.strategies
  import torch
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from hypothesis import assume, given, settings, Verbosity
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.fbgemm_qcomm_codec import CommType, QCommsConfig
  from torchrec.distributed.planner import ParameterConstraints
  from torchrec.distributed.test_utils.multi_process import MultiProcessTestBase
  from torchrec.distributed.test_utils.test_model import TestSparseNNBase
  from torchrec.distributed.test_utils.test_sharding import sharding_single_rank_test
  from torchrec.distributed.tests.test_sequence_model import TestEmbeddingCollectionSharder, TestSequenceSparseNN
  from torchrec.distributed.types import ShardingType
  from torchrec.modules.embedding_configs import EmbeddingConfig
  from torchrec.test_utils import seed_and_log, skip_if_asan_class
label_desc: Use image node_id 7 for calling the SequenceModelParallelTest obj's underlying member method code with example usage: obj.setUp() without return types