code: def dense_layer_2d(
      input_tensor, output_size, initializer, activation, name=None
  ):
      """A dense layer with 2D kernel.
  
      Args:
        input_tensor: Float tensor with rank 3.
        output_size: The size of output dimension.
        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, output_size],
              initializer=initializer,
          )
          b = tf.get_variable(
              name="bias",
              shape=[output_size],
              initializer=tf.zeros_initializer,
          )
  
      ret = tf.einsum("abc,cd->abd", 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 13 for calling a global function with example usage: dense_layer_2d(input_tensor, output_size, initializer, activation, name) and returns: activation, ret