code: def top_k_op(predictions, targets, k=1):
      """top_k_op.
  
      An op that calculates top-k mean accuracy.
  
      Examples:
          ```python
          input_data = placeholder(shape=[None, 784])
          y_pred = my_network(input_data) # Apply some ops
          y_true = placeholder(shape=[None, 10]) # Labels
          top3_op = top_k_op(y_pred, y_true, 3)
  
          # Calculate Top-3 accuracy by feeding data X and labels Y
          top3_accuracy = sess.run(top3_op, feed_dict={input_data: X, y_true: Y})
          ```
  
      Arguments:
          predictions: `Tensor`.
          targets: `Tensor`.
          k: `int`. Number of top elements to look at for computing precision.
  
      Returns:
          `Float`. The top-k mean accuracy.
  
      """
      with tf.name_scope("Top_" + str(k)):
          targets = tf.cast(targets, tf.int32)
          correct_pred = tf.nn.in_top_k(
              predictions, tf.argmax(targets, 1), k
          )
          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 4 for calling a global function with example usage: top_k_op(predictions, targets, k) and returns: acc