code: def split_heads(self, x, k=False):
      new_x_shape = x.size()[:-1] + (
          self.n_head,
          x.size(-1) // self.n_head,
      )
      x = x.view(*new_x_shape)  # in Tensorflow implem: fct split_states
      if k:
          return x.permute(
              0, 2, 3, 1
          )  # (batch, head, head_features, seq_length)
      else:
          return x.permute(0, 2, 1, 3)
  
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 5 for calling the Attention obj's underlying member method code with example usage: obj.split_heads(x, k) and returns: x, x