class_docstr: Sharded implementation of `EmbeddingBagCollection`.
  This is part of the public API to allow for manual data dist pipelining.
code: def create_context(self) -> NullShardedModuleContext:
      if is_torchdynamo_compiling():
          # Context creation is not supported by dynamo yet.
          # Context is not needed for TW sharding =>
          # Unblocking dynamo TW with None.
          # pyre-ignore
          return None
  
      return NullShardedModuleContext()
  
imports: from typing import Any, Dict, List, Optional, Type
  import torch
  from fbgemm_gpu.split_table_batched_embeddings_ops_inference import IntNBitTableBatchedEmbeddingBagsCodegen
  from torch import nn
  from torchrec.distributed.embedding_lookup import EmbeddingComputeKernel
  from torchrec.distributed.embedding_sharding import EmbeddingSharding, EmbeddingShardingInfo
  from torchrec.distributed.embedding_types import BaseQuantEmbeddingSharder, FeatureShardingMixIn, GroupedEmbeddingConfig, KJTList, ListOfKJTList
  from torchrec.distributed.embeddingbag import construct_output_kt, create_sharding_infos_by_sharding
  from torchrec.distributed.fused_params import FUSED_PARAM_QUANT_STATE_DICT_SPLIT_SCALE_BIAS, FUSED_PARAM_REGISTER_TBE_BOOL, get_tbes_to_register_from_iterable, is_fused_param_quant_state_dict_split_scale_bias, is_fused_param_register_tbe
  from torchrec.distributed.quant_state import ShardedQuantEmbeddingModuleState
  from torchrec.distributed.sharding.cw_sharding import InferCwPooledEmbeddingSharding
  from torchrec.distributed.sharding.rw_sharding import InferRwPooledEmbeddingSharding
  from torchrec.distributed.sharding.tw_sharding import InferTwEmbeddingSharding
  from torchrec.distributed.types import NullShardedModuleContext, NullShardingContext, ParameterSharding, ShardingEnv, ShardingType
  from torchrec.distributed.utils import copy_to_device
  from torchrec.modules.embedding_configs import data_type_to_sparse_type, dtype_to_data_type, EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollectionInterface
  from torchrec.modules.feature_processor_ import FeatureProcessorsCollection
  from torchrec.quant.embedding_modules import EmbeddingBagCollection, FeatureProcessedEmbeddingBagCollection, MODULE_ATTR_QUANT_STATE_DICT_SPLIT_SCALE_BIAS
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 15 for calling the ShardedQuantEmbeddingBagCollection obj's underlying member method code with example usage: obj.create_context() and returns: NullShardedModuleContext, None