Tensorflow API: What does the metric `tf.keras.metrics.TopKCategoricalAccuracy` do? According to the API doc, this metric


  "Computes how often targets are in the top K predictions."


But how come the following codes prouce the result 1?
0.95>0.9>0.8>0.1>0.05, both 0.95 and 0.8 lead to 1 in prediction, shouldn't the result be 2?

m = tf.keras.metrics.TopKCategoricalAccuracy()
m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
print('Final result: ', m.result().numpy())  # Final result: 1.0
Result of tf.keras.metrics.TopKCategoricalAccuracy() will be between 0 &amp; 1. Default value of the argument k is 5.
The result is 1 because for both the samples, the actual value is within the top 5 predictions.