Bincount

## Arguments

- self
(Tensor) 1-d int tensor

- weights
(Tensor) optional, weight for each value in the input tensor. Should be of same size as input tensor.

- minlength
(int) optional, minimum number of bins. Should be non-negative.

## bincount(input, weights=NULL, minlength=0) -> Tensor

Count the frequency of each value in an array of non-negative ints.

The number of bins (size 1) is one larger than the largest value in
`input`

unless `input`

is empty, in which case the result is a
tensor of size 0. If `minlength`

is specified, the number of bins is at least
`minlength`

and if `input`

is empty, then the result is tensor of size
`minlength`

filled with zeros. If `n`

is the value at position `i`

,
`out[n] += weights[i]`

if `weights`

is specified else
`out[n] += 1`

.

.. include:: cuda_deterministic.rst

## Examples

```
if (torch_is_installed()) {
input = torch_randint(1, 8, list(5), dtype=torch_int64())
weights = torch_linspace(0, 1, steps=5)
input
weights
torch_bincount(input, weights)
input$bincount(weights)
}
#> torch_tensor
#> 0.0000
#> 0.0000
#> 0.5000
#> 0.2500
#> 1.7500
#> [ CPUFloatType{5} ]
```