code: def __init__(self, sharding_type: str, kernel_type: str) -> None:
      super().__init__()
      self._sharding_type = sharding_type
      self._kernel_type = kernel_type
  
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 to create a new TestQuantECSharder object from inherited base classes: QuantEmbeddingCollectionSharder with example: obj = TestQuantECSharder(sharding_type, kernel_type)