class_docstr: This is a basic pruner, and in some papers it is called magnitude pruning or fine-grained pruning.
  It will mask the smallest magnitude weights in each specified layer by a saprsity ratio configured in the config list.
  
  Parameters
  ----------
  model
      Model to be pruned.
  config_list
      A list of dict, each dict configure which module need to be pruned, and how to prune.
      Please refer :doc:`Compression Config Specification </compression/config_list>` for more information.
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code: def _set_default_sparse_granularity(
      self, target_space: PruningTargetSpace
  ):
      return None
  
imports: from __future__ import annotations
  import logging
  from typing import Dict, List, Literal, Tuple, overload
  import torch
  from .tools import _DATA, _METRICS, _MASKS, active_sparse_targets_filter, norm_metrics, fpgm_metrics, generate_sparsity
  from ..base.compressor import Compressor, Pruner
  from ..base.target_space import PruningTargetSpace
  from ..base.wrapper import ModuleWrapper
  from ..utils.evaluator import Evaluator
label_desc: Use image node_id 1 for calling the LevelPruner obj's underlying member method code with example usage: obj._set_default_sparse_granularity(target_space) and returns: None