code: def __init__(
      self,
      sharding_type: str,
      kernel_type: str,
      qcomms_config: Optional[QCommsConfig] = None,
  ) -> None:
      self._sharding_type = sharding_type
      self._kernel_type = kernel_type
  
      qcomm_codecs_registry = {}
      if qcomms_config is not None:
          qcomm_codecs_registry = get_qcomm_codecs_registry(
              qcomms_config
          )
  
      fused_params = {"learning_rate": 0.1}
      super().__init__(
          fused_params=fused_params,
          qcomm_codecs_registry=qcomm_codecs_registry,
      )
  
imports: from typing import cast, Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torchrec.distributed.embedding import EmbeddingCollectionSharder
  from torchrec.distributed.fbgemm_qcomm_codec import get_qcomm_codecs_registry, QCommsConfig
  from torchrec.distributed.test_utils.test_model import ModelInput, TestDenseArch, TestSparseNNBase
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, EmbeddingConfig
  from torchrec.modules.embedding_modules import EmbeddingCollection
  from torchrec.modules.embedding_tower import EmbeddingTower
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor
label_desc: Use image node_id 1 to create a new TestEmbeddingCollectionSharder object from inherited base classes: EmbeddingCollectionSharder with example: obj = TestEmbeddingCollectionSharder(sharding_type, kernel_type, qcomms_config)