# tensorplay.masked.logaddexp

Source: https://www.tensorplay.cn/docs/generated/tensorplay.masked.logaddexp.html

```python
tensorplay.masked.logaddexp(input, other, *, dtype=None, input_mask=None, other_mask=None) → Tensor
```

Returns logaddexp of all the elements in the input and the other
tensor. The input elements are masked out according to the boolean tensor
input_mask and the attr:other elements are masked out according to the boolean tensor
other_mask.

The shapes of a mask tensor and the tensor to be masked
don’t need to match, but they must be broadcastable under the standard
broadcasting rules and the dimensionality of the mask
tensor must not be greater than of the tensor to be masked.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the input tensor

- other ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the second input tensor

Keyword Arguments:

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType), optional) – the desired data type of returned tensor. If specified, the output tensor is casted to dtype after the operation is performed. Default: None.

- input_mask ([tensorplay.Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the boolean tensor containing the binary mask of validity of input tensor elements. Default: None that is equivalent to tensorplay.ones(input.shape, dtype=tensorplay.bool).

- other_mask ([tensorplay.Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the boolean tensor containing the binary mask of validity of other tensor elements. Default: None that is equivalent to tensorplay.ones(other.shape, dtype=tensorplay.bool).

Example:

```
>>> input = tensorplay.tensor([-100.0, -200, -300])
>>> input
tensor([-100., -200., -300.])
>>> other = tensorplay.tensor([-1.0, -2, -3])
>>> other
tensor([-1., -2., -3.])
>>> mask = tensorplay.tensor([True, False, True])
>>> mask
tensor([ True, False,  True])
>>> tensorplay.masked._ops.logaddexp(input, other, input_mask=mask, other_mask=mask)
tensor([-1., -inf, -3.])
```