class_docstr: Wrapper Module for benchmarking TorchRec Inference Modules
code: def forward(self, input: KeyedJaggedTensor) -> KeyedTensor:
      return self._module.forward(input)
  
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 2 for calling the EBCWrapper obj's underlying member method code with example usage: obj.forward(input) and returns: self