code: def _get_model_and_sparsifier_and_sparse_config(qconfig=None):
      model = nn.Sequential(
          nn.Linear(4, 4),  # 0
          nn.ReLU(),
          nn.Linear(4, 4),  # 2
          nn.ReLU(),
          tq.QuantStub(),
          nn.Linear(4, 4),  # 5
          nn.ReLU(),
          tq.DeQuantStub(),
      )
      if qconfig:
          model[4].qconfig = qconfig
          model[5].qconfig = qconfig
  
      sparsifier = pruning.WeightNormSparsifier(**sparse_defaults)
  
      sparse_config = [
          {
              "tensor_fqn": "5.weight",
              "sparsity_level": 0.7,
              "sparse_block_shape": (1, 4),
              "zeros_per_block": 4,
          },
          {"tensor_fqn": "0.weight"},
      ]
      return model, sparsifier, sparse_config
  
imports: import logging
  import torch
  import torch.ao.quantization
  from torch import nn
  from torch.ao import pruning
  from torch.testing._internal.common_utils import TestCase
  from torch.ao.quantization.quantize_fx import prepare_fx, convert_fx, convert_to_reference_fx, prepare_qat_fx
  from torch.ao.pruning import fqn_to_module
label_desc: Use image node_id 1 for calling a global function with example usage: _get_model_and_sparsifier_and_sparse_config(qconfig) and returns: model,  sparsifier,  sparse_config