Div

## Arguments

- self
(Tensor) the input tensor.

- other
(Number) the number to be divided to each element of

`input`

- rounding_mode
(str, optional) – Type of rounding applied to the result:

`NULL`

- default behavior. Performs no rounding and, if both input and other are integer types, promotes the inputs to the default scalar type. Equivalent to true division in Python (the / operator) and NumPy’s`np.true_divide`

."trunc" - rounds the results of the division towards zero. Equivalent to C-style integer division.

"floor" - rounds the results of the division down. Equivalent to floor division in Python (the // operator) and NumPy’s

`np.floor_divide`

.

## div(input, other, out=NULL) -> Tensor

Divides each element of the input `input`

with the scalar `other`

and
returns a new resulting tensor.

Each element of the tensor `input`

is divided by each element of the tensor
`other`

. The resulting tensor is returned.

$$
\mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
$$
The shapes of `input`

and `other`

must be broadcastable
. If the `torch_dtype`

of `input`

and
`other`

differ, the `torch_dtype`

of the result tensor is determined
following rules described in the type promotion documentation
. If `out`

is specified, the result must be
castable to the `torch_dtype`

of the
specified output tensor. Integral division by zero leads to undefined behavior.

## Warning

Integer division using div is deprecated, and in a future release div will
perform true division like `torch_true_divide()`

.
Use `torch_floor_divide()`

to perform integer division,
instead.

$$
\mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
$$
If the `torch_dtype`

of `input`

and `other`

differ, the
`torch_dtype`

of the result tensor is determined following rules
described in the type promotion documentation . If
`out`

is specified, the result must be castable
to the `torch_dtype`

of the specified output tensor. Integral division
by zero leads to undefined behavior.

## Examples

```
if (torch_is_installed()) {
a = torch_randn(c(5))
a
torch_div(a, 0.5)
a = torch_randn(c(4, 4))
a
b = torch_randn(c(4))
b
torch_div(a, b)
}
#> torch_tensor
#> 1.1601 1.5019 0.0911 0.1775
#> 1.3729 -0.6050 -2.3711 0.5994
#> 0.0030 -4.3359 -0.0869 0.6405
#> -0.2648 0.1925 0.6269 -0.0510
#> [ CPUFloatType{4,4} ]
```