# tensorplay.masked.cumprod

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

```python
tensorplay.masked.cumprod(input, dim, *, dtype=None, mask=None) → Tensor
```

Returns cumulative_prod of all the slices in the input tensor
along dim while the input elements are masked out
according to the boolean tensor mask.

Let x be a sequence of unmasked elements of one-dimensional slice
of the input tensor. Cumsum of i-th element in x is
defined as prod(x[:i]).

The boolean tensor mask defines the “validity” of
input tensor elements: if mask element is True then
the corresponding element in input tensor will be included in
cumulative_prod computation, otherwise the element is ignored.

The values of masked-out elements of the output tensor have undefined
value: it may or may not be set to zero or nan; the choice may correspond to
the value that leads to the most efficient storage of output
tensor.

The mask of the cumulative_prod output tensor can be computed as
tensorplay.broadcast_to(mask, input.shape).

The shapes of the mask tensor and the input tensor
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 input tensor.

Example:

```
>>> input = tensor([[-3., -2., -1.], [0., 1., 2.]])
>>> input
tensor([[-3., -2., -1.],
        [0., 1., 2.]])
>>> mask = tensor([[True, False, True], [False, False, False]], dtype=Bool)
>>> mask
tensor([[True, False, True],
        [False, False, False]], dtype=Bool)
>>> tensorplay.masked._ops.cumprod(input, 1, mask=mask)
tensor([[-3., -3., 3.],
        [1., 1., 1.]])
```