code: def create_attach_sparsifier(model, **sparse_config):
      """Create a DataNormSparsifier and the attach it to the model embedding layers
  
      Args:
          model (nn.Module)
              layer of the model that needs to be attached to the sparsifier
          sparse_config (Dict)
              Config to the DataNormSparsifier. Should contain the following keys:
                  - sparse_block_shape
                  - norm
                  - sparsity_level
      """
      data_norm_sparsifier = DataNormSparsifier(**sparse_config)
      for name, parameter in model.named_parameters():
          if "emb_l" in name:
              valid_name = get_valid_name(name)
              data_norm_sparsifier.add_data(
                  name=valid_name, data=parameter
              )
      return data_norm_sparsifier
  
file_path: pytorch/torch/ao/pruning/_experimental/data_sparsifier/benchmarks/evaluate_disk_savings.py
imports: from typing import Dict, List
  import torch
  import time
  from torch.ao.pruning._experimental.data_sparsifier import DataNormSparsifier
  import os
  from dlrm_utils import get_dlrm_model, get_valid_name
  import copy
  import zipfile
  from zipfile import ZipFile
  import pandas
  import argparse
label_desc: Use image node_id 1 for calling a global function with example usage: create_attach_sparsifier(model) and returns: data_norm_sparsifier