code: def split_into_heads(self, x, batch_size):
      x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
      return tf.transpose(x, perm=[0, 2, 1, 3])
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list, stable_softmax
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_ctrl import CTRLConfig
label_desc: Use image node_id 2 for calling the TFMultiHeadAttention obj's underlying member method code with example usage: obj.split_into_heads(x, batch_size) and returns: tf