code: def test_sharding_nccl_tw(
      self,
      sharding_type: str,
      kernel_type: str,
      quant_state_dict_split_scale_bias: bool,
  ) -> None:
      self._test_sharding(
          sharders=[
              TestQuantECSharder(
                  sharding_type=sharding_type,
                  kernel_type=kernel_type,
              )
          ],
          backend="nccl",
          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 1 for calling the QuantSequenceModelParallelTest obj's underlying member method code with example usage: obj.test_sharding_nccl_tw(sharding_type, kernel_type, quant_state_dict_split_scale_bias) without return types