code: def dense_layer_3d(
      input_tensor,
      num_attention_heads,
      size_per_head,
      initializer,
      activation,
      name=None,
  ):
      """A dense layer with 3D kernel.
  
      Args:
        input_tensor: float Tensor of shape [batch, seq_length, hidden_size].
        num_attention_heads: Number of attention heads.
        size_per_head: The size per attention head.
        initializer: Kernel initializer.
        activation: Actication function.
        name: The name scope of this layer.
  
      Returns:
        float logits Tensor.
      """
  
      last_dim = get_shape_list(input_tensor)[-1]
  
      with tf.variable_scope(name):
          w = tf.get_variable(
              name="kernel",
              shape=[last_dim, num_attention_heads * size_per_head],
              initializer=initializer,
          )
          w = tf.reshape(
              w, [last_dim, num_attention_heads, size_per_head]
          )
          b = tf.get_variable(
              name="bias",
              shape=[num_attention_heads * size_per_head],
              initializer=tf.zeros_initializer,
          )
          b = tf.reshape(b, [num_attention_heads, size_per_head])
          ret = tf.einsum("abc,cde->abde", 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 11 for calling a global function with example usage: dense_layer_3d(input_tensor, num_attention_heads, size_per_head, initializer, activation, name) and returns: activation, ret