code: def dense_layer_3d_proj(
      input_tensor,
      hidden_size,
      num_attention_heads,
      head_size,
      initializer,
      activation,
      name=None,
  ):
      """A dense layer with 3D kernel for projection.
  
      Args:
        input_tensor: float Tensor of shape [batch,from_seq_length,
          num_attention_heads, size_per_head].
        hidden_size: The size of hidden layer.
        num_attention_heads: The size of output dimension.
        head_size: The size of head.
        initializer: Kernel initializer.
        activation: Actication function.
        name: The name scope of this layer.
  
      Returns:
        float logits Tensor.
      """
      head_size = hidden_size // num_attention_heads
      with tf.variable_scope(name):
          w = tf.get_variable(
              name="kernel",
              shape=[hidden_size, hidden_size],
              initializer=initializer,
          )
          w = tf.reshape(
              w, [num_attention_heads, head_size, hidden_size]
          )
          b = tf.get_variable(
              name="bias",
              shape=[hidden_size],
              initializer=tf.zeros_initializer,
          )
  
      ret = tf.einsum("BFNH,NHD->BFD", input_tensor, w)
      ret += b
      if activation is not None:
          return activation(ret)
      else:
          return ret
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import copy
  import json
  import math
  import re
  import numpy
  import six
  import tensorflow.compat.v1
label_desc: Use image node_id 12 for calling a global function with example usage: dense_layer_3d_proj(input_tensor, hidden_size, num_attention_heads, head_size, initializer, activation, name) and returns: activation, ret