Elementwise LogAddExp Operator. Add of exponentiations of the inputs The equation is:

\[Out=log(X.exp()+Y.exp())\]

\$X\$ the tensor of any dimension. \$Y\$ the tensor whose dimensions must be less than or equal to the dimensions of \$X\$.

There are two cases for this operator:

1. The shape of \$Y\$ is the same with \$X\$.

2. The shape of \$Y\$ is a continuous subsequence of \$X\$.

For case 2:

1. Broadcast \$Y\$ to match the shape of \$X\$, where axis is the start dimension index for broadcasting \$Y\$ onto \$X\$.

2. If \$axis\$ is -1 (default), \$axis\$=rank(\$X\$)-rank(\$Y\$).

3. The trailing dimensions of size 1 for \$Y\$ will be ignored for the consideration of subsequence, such as shape(\$Y\$) = (2, 1) => (2).

For example:

```shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
```
Parameters
• x (Tensor) – Tensor or LoDTensor of any dimensions. Its dtype should be int32, int64, float32, float64, float16.

• y (Tensor) – Tensor or LoDTensor of any dimensions. Its dtype should be int32, int64, float32, float64, float16.

• name (string, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Returns

N-D Tensor. A location into which the result is stored. It’s dimension equals with x.

Examples

```>>> import paddle

>>> x = paddle.to_tensor([-1, -2, -3], 'float64')