docstr: Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
docstr_tok: ["Prunes","heads","of","the","model",".","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. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
      """
      for layer, heads in heads_to_prune.items():
          self.h[layer].attn.prune_heads(heads)
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  from .configuration_gpt2 import GPT2Config
  from fastNLP.transformers.torch.activations import ACT2FN
  from fastNLP.transformers.torch.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from fastNLP.transformers.torch.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from fastNLP.transformers.torch.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from fastNLP.transformers.torch.utils.model_parallel_utils import assert_device_map, get_device_map
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP.core.log import logger
label_desc: Use image node_id 6 for calling the GPT2Model obj's underlying member method code with example usage: obj._prune_heads(heads_to_prune) without return types