docstr: Prunes heads of the model.
  
  Args:
      heads_to_prune:
          dict of {layer_num: list of heads to prune in this layer}
docstr_tok: ["Prunes","heads","of","the","model",".","Args",":","heads_to_prune",":","dict","of","{","layer_num",":","list","of","heads","to","prune","in","this","layer","}"]
code: def _prune_heads(self, heads_to_prune):
      """
      Prunes heads of the model.
  
      Args:
          heads_to_prune:
              dict of {layer_num: list of heads to prune in this layer}
      """
      for layer, heads in heads_to_prune.items():
          self.encoder.layer[layer].attention.prune_heads(heads)
  
imports: import math
  from typing import Optional, Set, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_vivit import VivitConfig
label_desc: Use image node_id 3 for calling the VivitModel obj's underlying member method code with example usage: obj._prune_heads(heads_to_prune) without return types