code: def _fx_unwrap_optional_tensor(
      optional: Optional[torch.Tensor],
  ) -> torch.Tensor:
      assert (
          optional is not None
      ), "Expected optional to be non-None Tensor"
      return optional
  
imports: from collections import defaultdict, deque
  from dataclasses import dataclass
  from typing import Any, cast, Dict, List, Optional, Tuple, Type
  import torch
  from fbgemm_gpu.split_table_batched_embeddings_ops_inference import IntNBitTableBatchedEmbeddingBagsCodegen
  from torch import nn
  from torchrec.distributed.embedding import create_sharding_infos_by_sharding, EmbeddingShardingInfo
  from torchrec.distributed.embedding_sharding import EmbeddingSharding
  from torchrec.distributed.embedding_types import BaseQuantEmbeddingSharder, FeatureShardingMixIn, GroupedEmbeddingConfig, KJTList, ListOfKJTList, ShardingType
  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_sequence_sharding import InferCwSequenceEmbeddingSharding
  from torchrec.distributed.sharding.rw_sequence_sharding import InferRwSequenceEmbeddingSharding
  from torchrec.distributed.sharding.rw_sharding import InferRwSparseFeaturesDist
  from torchrec.distributed.sharding.sequence_sharding import InferSequenceShardingContext
  from torchrec.distributed.sharding.tw_sequence_sharding import InferTwSequenceEmbeddingSharding
  from torchrec.distributed.types import ParameterSharding, ShardingEnv, ShardMetadata
  from torchrec.modules.embedding_configs import data_type_to_sparse_type, dtype_to_data_type, EmbeddingConfig
  from torchrec.quant.embedding_modules import EmbeddingCollection, MODULE_ATTR_QUANT_STATE_DICT_SPLIT_SCALE_BIAS
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor
  from torchrec.streamable import Multistreamable
label_desc: Use image node_id 2 for calling a global function with example usage: _fx_unwrap_optional_tensor(optional) and returns: optional