code: def prune_heads(self, heads):
      if len(heads) == 0:
          return
      heads, index = find_pruneable_heads_and_indices(
          heads, self.num_heads, self.head_dim, 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.num_heads) * (
          self.num_heads - len(heads)
      )
      self.num_heads = self.num_heads - len(heads)
      self.pruned_heads = self.pruned_heads.union(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 2 for calling the GPT2Attention obj's underlying member method code with example usage: obj.prune_heads(heads) without return types