class_docstr: Prune to an exact pruning level specification
  
  Attributes
  ----------
  mask_dict : dict
      Dictionary for saving masks, `key` should be layer name and
      `value` should be a tensor which has the same shape with layer's weight
code: def __init__(self, model, config_list, optimizer=None):
      super().__init__(model, config_list, optimizer)
      if optimizer is not None:
          self.patch_optimizer(self.update_mask)
  
imports: import types
  import logging
  import torch
  from .None import default_layers
label_desc: Use image node_id 1 to create a new Pruner object from inherited base classes: Compressor with example: obj = Pruner(model, config_list, optimizer)