code: def xdoc_pos_embed(
      input_context, positional_channels, batch_size, seq_length, width
  ):
      layoutlm_pos = layoutlm_pos_embed(
          input_context,
          positional_channels,
          batch_size,
          seq_length,
          width,
      )
      doc_layer1_out = tf.compat.v1.layers.dense(
          layoutlm_pos,
          units=width,
          name="doc_linear1",
          activation=tf.nn.relu,
      )
      return tf.compat.v1.layers.dense(
          doc_layer1_out, units=width, name="doc_linear2"
      )
  
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 10 for calling a global function with example usage: xdoc_pos_embed(input_context, positional_channels, batch_size, seq_length, width) and returns: tf