code: def split_into_heads(self, x, batch_size):
      x = x.reshape(batch_size, -1, self.num_heads, self.depth)
      return x.permute([0, 2, 1, 3])
  
imports: from typing import Optional, Tuple, Union
  import numpy
  import torch
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import Conv1D, 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_ctrl import CTRLConfig
label_desc: Use image node_id 3 for calling the MultiHeadAttention obj's underlying member method code with example usage: obj.split_into_heads(x, batch_size) and returns: x