def in_top_k(targets, predictions, k):
    """Checks if the targets are in the top-k predictions.

    Args:
        targets: A tensor of true labels.
        predictions: A tensor of predicted labels.
        k: An integer representing the number of predictions to consider.

    Returns:
        A boolean tensor of the same shape as `targets`, where each element
        indicates whether the corresponding target is in the top-k predictions.

    Example:

    >>> targets = keras.ops.convert_to_tensor([2, 5, 3])
    >>> predictions = keras.ops.convert_to_tensor(
    ... [[0.1, 0.4, 0.6, 0.9, 0.5],
    ...  [0.1, 0.7, 0.9, 0.8, 0.3],
    ...  [0.1, 0.6, 0.9, 0.9, 0.5]])
    >>> in_top_k(targets, predictions, k=3)
    array([ True False  True], shape=(3,), dtype=bool)
    """
    if any_symbolic_tensors((targets, predictions)):
        return InTopK(k).symbolic_call(targets, predictions)
    return backend.math.in_top_k(targets, predictions, k)
Checks if the targets are in the top-k predictions.

    Args:
        targets: A tensor of true labels.
        predictions: A tensor of predicted labels.
        k: An integer representing the number of predictions to consider.

    Returns:
        A boolean tensor of the same shape as `targets`, where each element
        indicates whether the corresponding target is in the top-k predictions.

    Example:

    >>> targets = keras.ops.convert_to_tensor([2, 5, 3])
    >>> predictions = keras.ops.convert_to_tensor(
    ... [[0.1, 0.4, 0.6, 0.9, 0.5],
    ...  [0.1, 0.7, 0.9, 0.8, 0.3],
    ...  [0.1, 0.6, 0.9, 0.9, 0.5]])
    >>> in_top_k(targets, predictions, k=3)
    array([ True False  True], shape=(3,), dtype=bool)
    
keras/src/ops/math.py