code: def embedding_postprocessor(self, *args, **kwargs):
      def table_pos_embed(
          input_context,
          positional_channels,
          batch_size,
          seq_length,
          width,
      ):
          output = []
          for entry in (
              [0, 1]
              if self.table_position_type == "row_col"
              else [0, 1, 2, 3]
          ):
              position_table = tf.compat.v1.get_variable(
                  name="pos_{}".format(entry),
                  shape=[self.max_row_col_embedding, width],
                  initializer=tf.compat.v1.random_normal_initializer(
                      stddev=1e-3
                  ),
              )
              position = tf.cast(
                  input_context[:, :, entry], dtype="int32"
              )
              output.append(tf.gather(position_table, position))
          return tf.math.add_n(output)
  
      return embedding_postprocessor(
          *args, pos2d_embedding_fn=table_pos_embed, **kwargs
      )
  
imports: import copy
  import json
  import math
  import numpy
  import tensorflow
  import functools
  from transformers.activations_tf import gelu
  from finetune.optimizers.recompute_grads import recompute_grad
  from finetune.nn.auxiliary import embed_position
label_desc: Use image node_id 2 for calling the TwinBertModel obj's underlying member method code with example usage: obj.embedding_postprocessor() and returns: embedding_postprocessor, tf