code: def __init__(
      self,
      sharding_type: str,
      kernel_type: str,
      fused_params: Optional[Dict[str, Any]] = None,
      shardable_params: Optional[List[str]] = None,
  ) -> None:
      super().__init__(
          fused_params=fused_params, shardable_params=shardable_params
      )
      self._sharding_type = sharding_type
      self._kernel_type = kernel_type
  
imports: import unittest
  from typing import Any, cast, Dict, List, Optional, Tuple
  import hypothesis.strategies
  import torch
  from hypothesis import given, settings, Verbosity
  from torch import nn, quantization
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel, ModuleSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology
  from torchrec.distributed.planner.enumerators import EmbeddingEnumerator
  from torchrec.distributed.planner.shard_estimators import EmbeddingPerfEstimator, EmbeddingStorageEstimator
  from torchrec.distributed.quant_embeddingbag import QuantEmbeddingBagCollectionSharder
  from torchrec.distributed.shard import _shard_modules
  from torchrec.distributed.test_utils.test_model import _get_default_rtol_and_atol, ModelInput, TestSparseNN
  from torchrec.distributed.types import ShardedModule, ShardingEnv, ShardingType
  from torchrec.distributed.utils import copy_to_device
  from torchrec.modules.embedding_configs import EmbeddingBagConfig, QuantConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.quant.embedding_modules import EmbeddingBagCollection, quant_prep_enable_quant_state_dict_split_scale_bias_for_types
  from torchrec.types import CopyMixIn
label_desc: Use image node_id 1 to create a new TestQuantEBCSharder object from inherited base classes: QuantEmbeddingBagCollectionSharder with example: obj = TestQuantEBCSharder(sharding_type, kernel_type, fused_params, shardable_params)