def silu(x):
    """Sigmoid Linear Unit (SiLU) activation function, also known as Swish.

    The SiLU activation function is computed by the sigmoid function multiplied
    by its input. It is defined as `f(x) = x * sigmoid(x)`.

    Args:
        x: Input tensor.

    Returns:
        A tensor with the same shape as `x`.

    Example:

    >>> x = keras.ops.convert_to_tensor([-6.0, 1.0, 0.0, 1.0, 6.0])
    >>> keras.ops.sigmoid(x)
    array([0.00247262, 0.7310586, 0.5, 0.7310586, 0.9975274], dtype=float32)
    >>> keras.ops.silu(x)
    array([-0.0148357, 0.7310586, 0.0, 0.7310586, 5.9851646], dtype=float32)

    """
    if any_symbolic_tensors((x,)):
        return Silu().symbolic_call(x)
    return backend.nn.silu(x)
Sigmoid Linear Unit (SiLU) activation function, also known as Swish.

    The SiLU activation function is computed by the sigmoid function multiplied
    by its input. It is defined as `f(x) = x * sigmoid(x)`.

    Args:
        x: Input tensor.

    Returns:
        A tensor with the same shape as `x`.

    Example:

    >>> x = keras.ops.convert_to_tensor([-6.0, 1.0, 0.0, 1.0, 6.0])
    >>> keras.ops.sigmoid(x)
    array([0.00247262, 0.7310586, 0.5, 0.7310586, 0.9975274], dtype=float32)
    >>> keras.ops.silu(x)
    array([-0.0148357, 0.7310586, 0.0, 0.7310586, 5.9851646], dtype=float32)

    
keras/src/ops/nn.py