code: def init_argparse() -> argparse.ArgumentParser:
      parser = argparse.ArgumentParser()
  
      parser.add_argument("--warmup_iters", type=int, default=20)
      parser.add_argument("--bench_iters", type=int, default=2000)
      parser.add_argument("--prof_iters", type=int, default=20)
      parser.add_argument("--batch_size", type=int, default=2048)
      parser.add_argument("--world_size", type=int, default=2)
      parser.add_argument(
          "--output_dir", type=str, default="/var/tmp/torchrec-bench"
      )
      parser.add_argument("--num_benchmarks", type=int, default=9)
  
      return parser
  
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 6 for calling a global function with example usage: init_argparse() and returns: parser