code: def accuracy_op(predictions, targets):
      """accuracy_op.
  
      An op that calculates mean accuracy, assuming predictiosn are targets
      are both one-hot encoded.
  
      Examples:
          ```python
          input_data = placeholder(shape=[None, 784])
          y_pred = my_network(input_data) # Apply some ops
          y_true = placeholder(shape=[None, 10]) # Labels
          acc_op = accuracy_op(y_pred, y_true)
  
          # Calculate accuracy by feeding data X and labels Y
          accuracy = sess.run(acc_op, feed_dict={input_data: X, y_true: Y})
          ```
  
      Arguments:
          predictions: `Tensor`.
          targets: `Tensor`.
  
      Returns:
          `Float`. The mean accuracy.
  
      """
      if not isinstance(targets, tf.Tensor):
          raise ValueError(
              "mean_accuracy 'input' argument only accepts type "
              "Tensor, '" + str(type(input)) + "' given."
          )
  
      with tf.name_scope("Accuracy"):
          correct_pred = tf.equal(
              tf.argmax(predictions, 1), tf.argmax(targets, 1)
          )
          acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
      return acc
  
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 2 for calling a global function with example usage: accuracy_op(predictions, targets) and returns: acc