code: def create_model():
      batch_size = None
      seq_length = None
      hidden_size = 768
      num_attention_heads = 12
      size_per_head = int(hidden_size / num_attention_heads)
  
      layer_input = tf.placeholder(
          tf.float32, shape=(batch_size, seq_length, hidden_size)
      )
      # Tensor of shape [batch_size, from_seq_length, to_seq_length].
      attention_mask = tf.placeholder(
          tf.float32, shape=(batch_size, seq_length, seq_length)
      )
  
      # output_rnn = transformer_model(input_tensor=layer_input,
      #                               attention_mask=attention_mask,
      #                               hidden_size=hidden_size,
      #                               num_attention_heads=num_attention_heads,
      #                               intermediate_size=1280,
      #                               do_return_all_layers=False)
  
      out = transformer_model(
          input_tensor=layer_input,
          attention_mask=attention_mask,
          hidden_size=hidden_size,
          num_hidden_layers=3,
          num_attention_heads=num_attention_heads,
          intermediate_size=12802,
          intermediate_act_fn=gelu,
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          initializer_range=0.02,
          do_return_all_layers=False,
      )
      return out
  
file_path: delta/gcompiler/example/python/standard_transformer/train.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.python.framework import graph_util
  from model import *
  from summarize_graph import GraphSummary
label_desc: Use image node_id 1 for calling a global function with example usage: create_model() and returns: out