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
docstr: Load the state dict saved from unwrapped model.
  
  Parameters:
  -----------
  model_state : dict
      state dict saved from unwrapped model
docstr_tok: ["Load","the","state","dict","saved","from","unwrapped","model",".","Parameters",":","--","--","--","--","--","-","model_state",":","dict","state","dict","saved","from","unwrapped","model"]
code: def load_model_state_dict(self, model_state):
      """
      Load the state dict saved from unwrapped model.
  
      Parameters:
      -----------
      model_state : dict
          state dict saved from unwrapped model
      """
      if self.is_wrapped:
          self._unwrap_model()
          self.bound_model.load_state_dict(model_state)
          self._wrap_model()
      else:
          self.bound_model.load_state_dict(model_state)
  
imports: import types
  import logging
  import torch
  from .None import default_layers
label_desc: Use image node_id 8 for calling the Pruner obj's underlying member method code with example usage: obj.load_model_state_dict(model_state) without return types