code: def create_default_model_config():
      return DLRMModelConfig(
          dense_arch_layer_sizes=[512, 256, 64],
          dense_in_features=len(DEFAULT_INT_NAMES),
          embedding_dim=64,
          id_list_features_keys=DEFAULT_CAT_NAMES,
          num_embeddings_per_feature=[
              45833188,
              36746,
              17245,
              7413,
              20243,
              3,
              7114,
              1441,
              62,
              29275261,
              1572176,
              345138,
              10,
              2209,
              11267,
              128,
              4,
              974,
              14,
              48937457,
              11316796,
              40094537,
              452104,
              12606,
              104,
              35,
          ],
          over_arch_layer_sizes=[512, 512, 256, 1],
      )
  
file_path: serve/examples/torchrec_dlrm/dlrm_factory.py
imports: import torch
  import torchrec.distributed
  from dlrm_model_config import DLRMModelConfig
  from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, ParameterConstraints, Topology
  from torchrec.distributed.quant_embeddingbag import QuantEmbeddingBagCollectionSharder
  from torchrec.distributed.types import ShardingType
  from torchrec.inference.modules import quantize_embeddings
  from torchrec.models.dlrm import DLRM
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
label_desc: Use image node_id 1 for calling a global function with example usage: create_default_model_config() and returns: DLRMModelConfig