code: def _test_sharding(
      self,
      sharders: List[TestQuantECSharder],
      backend: str = "gloo",
      world_size: int = 2,
      local_size: Optional[int] = None,
      model_class: Type[TestSparseNNBase] = TestSequenceSparseNN,
      quant_state_dict_split_scale_bias: bool = False,
  ) -> None:
      self._test_sharded_forward(
          world_size=world_size,
          model_class=cast(TestSparseNNBase, TestSequenceSparseNN),
          embedding_groups=self.embedding_groups,
          tables=self.tables,
          # pyre-ignore [6]
          sharders=sharders,
          quantize_callable=_quantize,
          quantize_callable_kwargs={
              "quant_state_dict_split_scale_bias": quant_state_dict_split_scale_bias
          },
      )
  
imports: import unittest
  from typing import cast, List, Optional, Type
  import hypothesis.strategies
  import torch
  from hypothesis import given, settings, Verbosity
  from torch import nn, quantization
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.quant_embedding import QuantEmbeddingCollectionSharder
  from torchrec.distributed.shard import _shard_modules
  from torchrec.distributed.test_utils.test_model import ModelInput, TestSparseNNBase
  from torchrec.distributed.test_utils.test_model_parallel_base import InferenceModelParallelTestBase
  from torchrec.distributed.tests.test_sequence_model import TestSequenceSparseNN
  from torchrec.distributed.types import ModuleSharder, ShardingEnv, ShardingType
  from torchrec.modules.embedding_configs import EmbeddingConfig
  from torchrec.modules.embedding_modules import EmbeddingCollection
  from torchrec.quant.embedding_modules import EmbeddingCollection, quant_prep_enable_quant_state_dict_split_scale_bias_for_types
  from torchrec.test_utils import seed_and_log, skip_if_asan_class
label_desc: Use image node_id 3 for calling the QuantSequenceModelParallelTest obj's underlying member method code with example usage: obj._test_sharding(sharders, backend, world_size, local_size, model_class, quant_state_dict_split_scale_bias) without return types