code: def multi_head(self, query, key, value):
      query = self.lin_q(query)
      key = self.lin_k(key)
      value = self.lin_v(value)
      out_channels_per_head = self.dim // self.heads
  
      query_shape = [
          -1,
          tf.shape(query)[1],
          self.heads,
          out_channels_per_head,
      ]
      query = tf.transpose(tf.reshape(query, query_shape), [1, 0, 2, 3])
  
      key_shape = [
          -1,
          tf.shape(key)[1],
          self.heads,
          out_channels_per_head,
      ]
      key = tf.transpose(tf.reshape(key, key_shape), [1, 0, 2, 3])
  
      value_shape = [
          -1,
          tf.shape(value)[1],
          self.heads,
          out_channels_per_head,
      ]
      value = tf.transpose(tf.reshape(value, value_shape), [1, 0, 2, 3])
  
      out = self.attention(query, key, value)
      out = tf.transpose(out, [1, 0, 2, 3])
      out_shape = [-1, tf.shape(query)[1], self.dim]
      out = tf.reshape(out, out_shape)
      return out
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import tf_euler
  from tf_euler.python.convolution import conv
  from tf_euler.python.euler_ops import mp_ops
label_desc: Use image node_id 4 for calling the DNAConv obj's underlying member method code with example usage: obj.multi_head(query, key, value) and returns: out