code: def __init__(
      self,
      module: Union[
          ManagedCollisionEmbeddingBagCollection,
          ManagedCollisionEmbeddingCollection,
      ],
      table_name_to_parameter_sharding: Dict[str, ParameterSharding],
      e_sharder: Union[
          EmbeddingBagCollectionSharder, EmbeddingCollectionSharder
      ],
      mc_sharder: ManagedCollisionCollectionSharder,
      # TODO - maybe we need this to manage unsharded/sharded consistency/state consistency
      env: ShardingEnv,
      device: torch.device,
  ) -> None:
      super().__init__()
  
      self._device = device
      self._env = env
  
      if isinstance(module, ManagedCollisionEmbeddingBagCollection):
          assert isinstance(e_sharder, EmbeddingBagCollectionSharder)
          assert isinstance(
              module._embedding_module, EmbeddingBagCollection
          )
          self.bagged: bool = True
  
          self._embedding_module: ShardedEmbeddingBagCollection = (
              e_sharder.shard(
                  module._embedding_module,
                  table_name_to_parameter_sharding,
                  env=env,
                  device=device,
              )
          )
      else:
          assert isinstance(e_sharder, EmbeddingCollectionSharder)
          assert isinstance(
              module._embedding_module, EmbeddingCollection
          )
          self.bagged: bool = False
  
          self._embedding_module: ShardedEmbeddingCollection = (
              e_sharder.shard(
                  module._embedding_module,
                  table_name_to_parameter_sharding,
                  env=env,
                  device=device,
              )
          )
      # TODO: This is a hack since _embedding_module doesn't need input
      # dist, so eliminating it so all fused a2a will ignore it.
      self._embedding_module._has_uninitialized_input_dist = False
      self._managed_collision_collection: ShardedManagedCollisionCollection = mc_sharder.shard(
          module._managed_collision_collection,
          table_name_to_parameter_sharding,
          env=env,
          device=device,
          # pyre-ignore
          sharding_type_to_sharding=self._embedding_module._sharding_type_to_sharding,
      )
      self._return_remapped_features: bool = (
          module._return_remapped_features
      )
  
      # pyre-ignore
      self._table_to_tbe_and_index = {}
      for lookup in self._embedding_module._lookups:
          for emb_module in lookup._emb_modules:
              for table_idx, table in enumerate(
                  emb_module._config.embedding_tables
              ):
                  self._table_to_tbe_and_index[table.name] = (
                      emb_module._emb_module,
                      torch.tensor(
                          [table_idx],
                          dtype=torch.int,
                          device=self._device,
                      ),
                  )
      self._buffer_ids: torch.Tensor = torch.tensor(
          [0], device=self._device, dtype=torch.int
      )
  
imports: import logging
  from typing import Dict, Iterator, List, Optional, Tuple, Type, TypeVar, Union
  import torch
  from torch.autograd.profiler import record_function
  from torchrec.distributed.embedding import EmbeddingCollectionSharder, ShardedEmbeddingCollection
  from torchrec.distributed.embedding_types import BaseEmbeddingSharder, EmbeddingComputeKernel, KJTList, ShardedEmbeddingModule
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder, ShardedEmbeddingBagCollection
  from torchrec.distributed.mc_modules import ManagedCollisionCollectionSharder, ShardedManagedCollisionCollection
  from torchrec.distributed.types import Awaitable, LazyAwaitable, Multistreamable, NoWait, ParameterSharding, QuantizedCommCodecs, ShardingEnv
  from torchrec.distributed.utils import append_prefix
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingCollection
  from torchrec.modules.mc_embedding_modules import BaseManagedCollisionEmbeddingCollection, ManagedCollisionEmbeddingBagCollection, ManagedCollisionEmbeddingCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 1 to create a new BaseShardedManagedCollisionEmbeddingCollection object from inherited base classes: ShardedEmbeddingModule with example: obj = BaseShardedManagedCollisionEmbeddingCollection(module, table_name_to_parameter_sharding, e_sharder, mc_sharder, env, device)