code: def setUp(self) -> None:
      self.parameter_sharding = ParameterSharding(
          sharding_type="data_parallel",
          compute_kernel="dense",
          ranks=[0, 1],
          sharding_spec=None,
          cache_params=CacheParams(
              algorithm=CacheAlgorithm.LFU,
              reserved_memory=1.0,
              prefetch_pipeline=False,
          ),
          enforce_hbm=False,
          stochastic_rounding=True,
          bounds_check_mode=BoundsCheckMode.WARNING,
      )
  
imports: import itertools
  import math
  import os
  import random
  import unittest
  from typing import cast, List, Optional, Tuple
  import torch
  import torch.distributed
  from hypothesis import given, settings, strategies, Verbosity
  from torchrec.distributed.embedding_sharding import bucketize_kjt_before_all2all
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.test_utils.test_model import TestSparseNN
  from torchrec.distributed.types import BoundsCheckMode, CacheAlgorithm, CacheParams, DataType, ModuleSharder, ParameterSharding
  from torchrec.distributed.utils import add_params_from_parameter_sharding, convert_to_fbgemm_types, get_unsharded_module_names, merge_fused_params
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from torchrec.sparse.test_utils import keyed_jagged_tensor_equals
  from torchrec.test_utils import get_free_port
label_desc: Use image node_id 1 for calling the AddParamsFromParameterShardingTest obj's underlying member method code with example usage: obj.setUp() without return types