elu:
  description: |
    Exponential Linear Unit activation function.

    Applies the exponential linear unit function element-wise.
    The activation function is defined as:

    .. math::

        \text{ELU}(x)= \left\{
        \begin{array}{align}
            \alpha(e^{x}  - 1) & \text{if } x \le 0\\
            x & \text{if } x \gt 0\\
        \end{array}\right.

    Where :math:`x` is the element of input Tensor `input_x`, :math:`\alpha` is param `alpha`,
    it determines the smoothness of ELU.
    The picture about ELU looks like this `ELU <https://en.wikipedia.org/wiki/
    Activation_function#/media/File:Activation_elu.svg>`_ .

    ELU function graph:

    .. image:: ../images/ELU.png
        :align: center

    Args:
        input_x (Tensor): The input of ELU is a Tensor of any dimension with data type of float16 or float32.
        alpha (float, optional): The alpha value of ELU, the data type is float. Only support ``1.0`` currently.
            Default: ``1.0`` .

    Returns:
        Tensor, has the same shape and data type as `input_x`.

    Raises:
        TypeError: If `alpha` is not a float.
        TypeError: If dtype of `input_x` is neither float16 nor float32.
        ValueError: If `alpha` is not equal to 1.0.

    Supported Platforms:
        ``Ascend`` ``GPU`` ``CPU``

    Examples:
        >>> import mindspore
        >>> import numpy as np
        >>> from mindspore import Tensor, ops
        >>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
        >>> output = ops.elu(x)
        >>> print(output)
        [[-0.63212055  4.         -0.99966455]
         [ 2.         -0.99326205  9.        ]]