code: def _get_sharding_type_abbr(sharding_type: str) -> str:
      if sharding_type == ShardingType.DATA_PARALLEL.value:
          return "DP"
      elif sharding_type == ShardingType.TABLE_WISE.value:
          return "TW"
      elif sharding_type == ShardingType.COLUMN_WISE.value:
          return "CW"
      elif sharding_type == ShardingType.ROW_WISE.value:
          return "RW"
      elif sharding_type == ShardingType.TABLE_ROW_WISE.value:
          return "TWRW"
      elif sharding_type == ShardingType.TABLE_COLUMN_WISE.value:
          return "TWCW"
      else:
          raise ValueError(
              f"Unrecognized or unsupported sharding type provided: {sharding_type}"
          )
  
imports: import logging
  from collections import defaultdict
  from typing import Any, cast, Dict, List, Optional, Tuple, Union
  from torch import nn
  from torchrec.distributed.planner.constants import BIGINT_DTYPE, NUM_POOLINGS
  from torchrec.distributed.planner.shard_estimators import _calculate_shard_io_sizes
  from torchrec.distributed.planner.storage_reservations import FixedPercentageStorageReservation, HeuristicalStorageReservation, InferenceStorageReservation
  from torchrec.distributed.planner.types import ParameterConstraints, Perf, ShardingOption, Stats, Storage, StorageReservation, Topology
  from torchrec.distributed.planner.utils import _find_imbalance_tables, bytes_to_gb, bytes_to_mb, sharder_name
  from torchrec.distributed.types import ModuleSharder, ParameterSharding, ShardingPlan, ShardingType
label_desc: Use image node_id 3 for calling a global function with example usage: _get_sharding_type_abbr(sharding_type) and returns: str, str, str, str, str, str