code: def prune_heads(self, heads):
      if len(heads) == 0:
          return
      heads, index = find_pruneable_heads_and_indices(
          heads,
          self.n_head,
          self.split_size // self.n_head,
          self.pruned_heads,
      )
      index_attn = torch.cat(
          [
              index,
              index + self.split_size,
              index + (2 * self.split_size),
          ]
      )
  
      # Prune conv1d layers
      self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
      self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
  
      # Update hyper params
      self.split_size = (self.split_size // self.n_head) * (
          self.n_head - len(heads)
      )
      self.n_head = self.n_head - len(heads)
      self.pruned_heads = self.pruned_heads.union(heads)
  
imports: import os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.prune_heads(heads) without return types