class_docstr: Sharded implementation of `EmbeddingCollection`.
  This is part of the public API to allow for manual data dist pipelining.
class_docstr_tok: ["Sharded","implementation","of","`","EmbeddingCollection","`",".","This","is","part","of","the","public","API","to","allow","for","manual","data","dist","pipelining","."]
code: def create_context(self) -> EmbeddingCollectionContext:
      return EmbeddingCollectionContext(sharding_contexts=[])
  
imports: import copy
  import logging
  import warnings
  from collections import defaultdict, deque, OrderedDict
  from dataclasses import dataclass, field
  from itertools import accumulate
  from typing import Any, cast, Dict, List, MutableMapping, Optional, Type, Union
  import torch
  from torch import nn
  from torch.autograd.profiler import record_function
  from torch.nn.parallel import DistributedDataParallel
  from torchrec.distributed.embedding_sharding import EmbeddingSharding, EmbeddingShardingInfo, KJTListSplitsAwaitable
  from torchrec.distributed.embedding_types import BaseEmbeddingSharder, EmbeddingComputeKernel, KJTList, ShardedEmbeddingModule, ShardingType
  from torchrec.distributed.sharding.cw_sequence_sharding import CwSequenceEmbeddingSharding
  from torchrec.distributed.sharding.dp_sequence_sharding import DpSequenceEmbeddingSharding
  from torchrec.distributed.sharding.rw_sequence_sharding import RwSequenceEmbeddingSharding
  from torchrec.distributed.sharding.rw_sharding import RwSparseFeaturesDist
  from torchrec.distributed.sharding.sequence_sharding import SequenceShardingContext
  from torchrec.distributed.sharding.tw_sequence_sharding import TwSequenceEmbeddingSharding
  from torchrec.distributed.types import Awaitable, EmbeddingModuleShardingPlan, LazyAwaitable, Multistreamable, ParameterSharding, QuantizedCommCodecs, ShardedTensor, ShardingEnv, ShardMetadata
  from torchrec.distributed.utils import add_params_from_parameter_sharding, convert_to_fbgemm_types, merge_fused_params, optimizer_type_to_emb_opt_type
  from torchrec.modules.embedding_configs import EmbeddingConfig, EmbeddingTableConfig, PoolingType
  from torchrec.modules.embedding_modules import EmbeddingCollection, EmbeddingCollectionInterface
  from torchrec.optim.fused import EmptyFusedOptimizer, FusedOptimizerModule
  from torchrec.optim.keyed import CombinedOptimizer, KeyedOptimizer
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor
label_desc: Use image node_id 16 for calling the ShardedEmbeddingCollection obj's underlying member method code with example usage: obj.create_context() and returns: EmbeddingCollectionContext