code: def test_module_to_default_sharders(self) -> None:
      default_sharder_map = get_module_to_default_sharders()
      self.assertCountEqual(
          default_sharder_map,
          [
              EmbeddingBagCollection,
              FeatureProcessedEmbeddingBagCollection,
              EmbeddingCollection,
              FusedEmbeddingBagCollection,
              QuantEmbeddingBagCollection,
              QuantEmbeddingCollection,
              ManagedCollisionEmbeddingBagCollection,
              ManagedCollisionEmbeddingCollection,
          ],
      )
      self.assertIsInstance(
          default_sharder_map[EmbeddingBagCollection],
          EmbeddingBagCollectionSharder,
      )
      self.assertIsInstance(
          default_sharder_map[FeatureProcessedEmbeddingBagCollection],
          FeatureProcessedEmbeddingBagCollectionSharder,
      )
      self.assertIsInstance(
          default_sharder_map[EmbeddingCollection],
          EmbeddingCollectionSharder,
      )
      self.assertIsInstance(
          default_sharder_map[FusedEmbeddingBagCollection],
          FusedEmbeddingBagCollectionSharder,
      )
      self.assertIsInstance(
          default_sharder_map[QuantEmbeddingBagCollection],
          QuantEmbeddingBagCollectionSharder,
      )
      self.assertIsInstance(
          default_sharder_map[QuantEmbeddingCollection],
          QuantEmbeddingCollectionSharder,
      )
      self.assertIsInstance(
          default_sharder_map[ManagedCollisionEmbeddingBagCollection],
          ManagedCollisionEmbeddingBagCollectionSharder,
      )
  
      self.assertIsInstance(
          default_sharder_map[ManagedCollisionEmbeddingCollection],
          ManagedCollisionEmbeddingCollectionSharder,
      )
  
imports: import copy
  import unittest
  from typing import Any, Dict, List, Optional
  import hypothesis.strategies
  import torch
  from hypothesis import given, settings, Verbosity
  from torchrec import distributed
  from torchrec.distributed.sharding_plan import column_wise, construct_module_sharding_plan, data_parallel, EmbeddingBagCollectionSharder, EmbeddingCollectionSharder, FeatureProcessedEmbeddingBagCollectionSharder, FusedEmbeddingBagCollectionSharder, get_module_to_default_sharders, ManagedCollisionEmbeddingBagCollectionSharder, ManagedCollisionEmbeddingCollectionSharder, ParameterShardingGenerator, QuantEmbeddingBagCollectionSharder, QuantEmbeddingCollectionSharder, row_wise, table_row_wise, table_wise
  from torchrec.distributed.test_utils.multi_process import MultiProcessContext, MultiProcessTestBase
  from torchrec.distributed.test_utils.test_sharding import copy_state_dict
  from torchrec.distributed.types import EmbeddingModuleShardingPlan, EnumerableShardingSpec, ParameterSharding, ShardingEnv, ShardingPlan, ShardingType, ShardMetadata
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingCollection
  from torchrec.modules.fp_embedding_modules import FeatureProcessedEmbeddingBagCollection
  from torchrec.modules.fused_embedding_modules import FusedEmbeddingBagCollection
  from torchrec.modules.mc_embedding_modules import ManagedCollisionEmbeddingBagCollection, ManagedCollisionEmbeddingCollection
  from torchrec.quant.embedding_modules import EmbeddingBagCollection, EmbeddingCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from torchrec.test_utils import skip_if_asan_class
label_desc: Use image node_id 2 for calling the ShardingPlanTest obj's underlying member method code with example usage: obj.test_module_to_default_sharders() without return types