code: def fc_op(
      input_op, name, n_out, layer_collector, act_func=tf.nn.leaky_relu
  ):
      n_in = input_op.get_shape()[-1].value
      with tf.name_scope(name) as scope:
          kernel = tf.Variable(
              tf.contrib.layers.xavier_initializer()([n_in, n_out]),
              dtype=tf.float32,
              name=scope + "w",
          )
  
          # kernel = tf.Variable(tf.random_normal([n_in, n_out]))
          biases = tf.Variable(
              tf.constant(0, shape=[1, n_out], dtype=tf.float32),
              name=scope + "b",
          )
  
          fc = tf.add(tf.matmul(input_op, kernel), biases)
          activation = act_func(fc, name=scope + "act")
          layer_collector.append([kernel, biases])
          return activation
  
file_path: OpenNE/src/openne/sdne.py
imports: import tensorflow
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: fc_op(input_op, name, n_out, layer_collector, act_func) and returns: activation