code: def benchmark_type_name(
      terminal_stage: TerminalStage, sharding_type: ShardingType
  ) -> str:
      if terminal_stage in UNSHARDED_TERMINAL_STAGES:
          name = "unsharded-qebc"
          if terminal_stage == TerminalStage.FXJIT_QUANT:
              name += "-fxjit-quant"
      else:
          if sharding_type == ShardingType.ROW_WISE:
              name = "rw-sharded-qebc"
          elif sharding_type == ShardingType.COLUMN_WISE:
              name = "cw-sharded-qebc"
          else:
              name = "tw-sharded-qebc"
  
          if terminal_stage == TerminalStage.FXJIT_SHARDED_QUANT:
              name += "-fxjit-quant"
  
      return name
  
imports: import argparse
  import gc
  import logging
  import os
  import time
  from dataclasses import dataclass
  from enum import Enum
  from typing import Dict, List, Tuple
  import torch
  from torch.autograd.profiler import record_function
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel, ShardingType
  from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology
  from torchrec.distributed.planner.enumerators import EmbeddingEnumerator
  from torchrec.distributed.planner.shard_estimators import EmbeddingPerfEstimator, EmbeddingStorageEstimator
  from torchrec.distributed.shard import _shard_modules
  from torchrec.distributed.test_utils.infer_utils import TestQuantEBCSharder
  from torchrec.distributed.test_utils.test_model import ModelInput
  from torchrec.distributed.types import DataType, ShardingEnv
  from torchrec.fx import symbolic_trace
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.quant.embedding_modules import EmbeddingBagCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 4 for calling a global function with example usage: benchmark_type_name(terminal_stage, sharding_type) and returns: name