code: def _sharded_quant_ebc_model() -> (
      Tuple[torch.nn.Module, List[KeyedJaggedTensor]]
  ):
      num_embeddings = 256
      emb_dim = 12
      world_size = 2
      batch_size = 4
      local_device = torch.device("cuda:0")
      mi = create_test_model_ebc_only(
          num_embeddings,
          emb_dim,
          world_size,
          batch_size,
          num_features=2,
          num_weighted_features=1,
          dense_device=local_device,
          sparse_device=local_device,
          quant_state_dict_split_scale_bias=True,
      )
      input_kjts = [
          inp.to(local_device).idlist_features
          for inp in prep_inputs(
              mi, world_size, batch_size, long_indices=False
          )
      ]
      model: torch.nn.Module = KJTInputExportWrapper(
          mi.quant_model, input_kjts[0].keys()
      )
  
      sharding_type: ShardingType = ShardingType.TABLE_WISE
      sharder = TestQuantEBCSharder(
          sharding_type=sharding_type.value,
          kernel_type=EmbeddingComputeKernel.QUANT.value,
          shardable_params=[table.name for table in mi.tables],
      )
      # pyre-ignore
      plan = mi.planner.plan(
          model,
          [sharder],
      )
      sharded_model = _shard_modules(
          module=model,
          # pyre-ignore
          sharders=[sharder],
          device=local_device,
          plan=plan,
          # pyre-ignore
          env=ShardingEnv.from_local(
              world_size=mi.topology.world_size, rank=0
          ),
      )
      return sharded_model, input_kjts
  
imports: import sys
  import unittest
  from typing import List, Tuple
  import torch
  import torch._dynamo.skipfiles
  from fbgemm_gpu import sparse_ops
  from torch._export import dynamic_dim
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.shard import _shard_modules
  from torchrec.distributed.test_utils.infer_utils import assert_close, create_test_model_ebc_only, dynamo_skipfiles_allow, KJTInputExportWrapper, prep_inputs, replace_registered_tbes_with_mock_tbes, replace_sharded_quant_modules_tbes_with_mock_tbes, TestQuantEBCSharder
  from torchrec.distributed.types import ShardingEnv, ShardingType
  from torchrec.sparse.jagged_tensor import ComputeKJTToJTDict, KeyedJaggedTensor
label_desc: Use image node_id 2 for calling a global function with example usage: _sharded_quant_ebc_model() and returns: sharded_model,  input_kjts