code: def positionwise_FF(
      inp,
      d_model,
      d_inner,
      dropout,
      kernel_initializer,
      scope="ff",
      is_training=True,
  ):
      output = inp
      with tf.variable_scope(scope):
          output = tf.layers.dense(
              inp,
              d_inner,
              activation=tf.nn.relu,
              kernel_initializer=kernel_initializer,
              bias_initializer=kernel_initializer,
              name="layer_1",
          )
          # output = tf.layers.dense(inp, 50,
          # kernel_initializer=kernel_initializer,
          # name='layer_1.1')
          # output = tf.layers.dense(output, d_inner, activation=tf.nn.relu,
          # kernel_initializer=kernel_initializer,
          # name='layer_1.2')
          output = tf.layers.dropout(
              output, dropout, training=is_training, name="drop_1"
          )
          # output = tf.layers.dense(output, d_model,
          # kernel_initializer=kernel_initializer,
          # name='layer_2')
          output = tf.layers.dense(
              output,
              50,
              kernel_initializer=kernel_initializer,
              bias_initializer=kernel_initializer,
              name="layer_2.1",
          )
          output = tf.layers.dense(
              output,
              d_model,
              kernel_initializer=kernel_initializer,
              bias_initializer=kernel_initializer,
              name="layer_2",
          )
          output = tf.layers.dropout(
              output, dropout, training=is_training, name="drop_2"
          )
          # ff_out_before = output
          output = tf.contrib.layers.layer_norm(
              output + inp, begin_norm_axis=-1
          )
      return output
  
file_path: delta/gcompiler/example/python/nlp_transformer/model.py
imports: import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: positionwise_FF(inp, d_model, d_inner, dropout, kernel_initializer, scope, is_training) and returns: output