def categorical_crossentropy(
    y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1
):
    """Computes the categorical crossentropy loss.

    Args:
        y_true: Tensor of one-hot true targets.
        y_pred: Tensor of predicted targets.
        from_logits: Whether `y_pred` is expected to be a logits tensor. By
            default, we assume that `y_pred` encodes a probability distribution.
        label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For
            example, if `0.1`, use `0.1 / num_classes` for non-target labels
            and `0.9 + 0.1 / num_classes` for target labels.
        axis: Defaults to `-1`. The dimension along which the entropy is
            computed.

    Returns:
        Categorical crossentropy loss value.

    Example:

    >>> y_true = [[0, 1, 0], [0, 0, 1]]
    >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
    >>> loss = keras.losses.categorical_crossentropy(y_true, y_pred)
    >>> assert loss.shape == (2,)
    >>> loss
    array([0.0513, 2.303], dtype=float32)
    """
    if isinstance(axis, bool):
        raise ValueError(
            "`axis` must be of type `int`. "
            f"Received: axis={axis} of type {type(axis)}"
        )
    y_pred = ops.convert_to_tensor(y_pred)
    y_true = ops.cast(y_true, y_pred.dtype)

    if y_pred.shape[-1] == 1:
        warnings.warn(
            "In loss categorical_crossentropy, expected "
            "y_pred.shape to be (batch_size, num_classes) "
            f"with num_classes > 1. Received: y_pred.shape={y_pred.shape}. "
            "Consider using 'binary_crossentropy' if you only have 2 classes.",
            SyntaxWarning,
            stacklevel=2,
        )

    if label_smoothing:
        num_classes = ops.cast(ops.shape(y_true)[-1], y_pred.dtype)
        y_true = y_true * (1.0 - label_smoothing) + (
            label_smoothing / num_classes
        )

    return ops.categorical_crossentropy(
        y_true, y_pred, from_logits=from_logits, axis=axis
    )
Computes the categorical crossentropy loss.

    Args:
        y_true: Tensor of one-hot true targets.
        y_pred: Tensor of predicted targets.
        from_logits: Whether `y_pred` is expected to be a logits tensor. By
            default, we assume that `y_pred` encodes a probability distribution.
        label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For
            example, if `0.1`, use `0.1 / num_classes` for non-target labels
            and `0.9 + 0.1 / num_classes` for target labels.
        axis: Defaults to `-1`. The dimension along which the entropy is
            computed.

    Returns:
        Categorical crossentropy loss value.

    Example:

    >>> y_true = [[0, 1, 0], [0, 0, 1]]
    >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
    >>> loss = keras.losses.categorical_crossentropy(y_true, y_pred)
    >>> assert loss.shape == (2,)
    >>> loss
    array([0.0513, 2.303], dtype=float32)
    
keras/src/losses/losses.py