code: def r2_op(predictions, targets):
      """r2_op.
  
      An op that calculates the standard error.
  
      Examples:
          ```python
          input_data = placeholder(shape=[None, 784])
          y_pred = my_network(input_data) # Apply some ops
          y_true = placeholder(shape=[None, 10]) # Labels
          stderr_op = r2_op(y_pred, y_true)
  
          # Calculate standard error by feeding data X and labels Y
          std_error = sess.run(stderr_op, feed_dict={input_data: X, y_true: Y})
          ```
  
      Arguments:
          predictions: `Tensor`.
          targets: `Tensor`.
  
      Returns:
          `Float`. The standard error.
  
      """
      with tf.name_scope("StandardError"):
          a = tf.reduce_sum(
              tf.square(tf.subtract(targets, predictions))
          )
          b = tf.reduce_sum(
              tf.square(tf.subtract(targets, tf.reduce_mean(targets)))
          )
          return tf.subtract(1.0, tf.divide(a, b))
  
imports: from __future__ import division, print_function, absolute_import
  from .utils import get_from_module
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling a global function with example usage: r2_op(predictions, targets) and returns: tf