docstr: Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
  class PreTrainedModel
docstr_tok: ["Prunes","heads","of","the","model",".","heads_to_prune",":","dict","of","{","layer_num",":","list","of","heads","to","prune","in","this","layer","}","See","base","class","PreTrainedModel"]
code: def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
      """
      Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
      class PreTrainedModel
      """
      for layer, heads in heads_to_prune.items():
          self.encoder.layer[layer].attention.prune_heads(heads)
  
imports: import collections.abc
  import math
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ...activations import ACT2FN
  from ...modeling_outputs import BackboneOutput, BaseModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ...utils.backbone_utils import BackboneMixin
  from .configuration_vitdet import VitDetConfig
label_desc: Use image node_id 3 for calling the VitDetModel obj's underlying member method code with example usage: obj._prune_heads(heads_to_prune) without return types