Min

## Arguments

- self
(Tensor) the input tensor.

- dim
(int) the dimension to reduce.

- keepdim
(bool) whether the output tensor has

`dim`

retained or not.- out
(tuple, optional) the tuple of two output tensors (min, min_indices)

- other
(Tensor) the second input tensor

## Note

When the shapes do not match, the shape of the returned output tensor follows the broadcasting rules .

## min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor)

Returns a namedtuple `(values, indices)`

where `values`

is the minimum
value of each row of the `input`

tensor in the given dimension
`dim`

. And `indices`

is the index location of each minimum value found
(argmin).

## Warning

`indices`

does not necessarily contain the first occurrence of each
minimal value found, unless it is unique.
The exact implementation details are device-specific.
Do not expect the same result when run on CPU and GPU in general.

If `keepdim`

is `TRUE`

, the output tensors are of the same size as
`input`

except in the dimension `dim`

where they are of size 1.
Otherwise, `dim`

is squeezed (see `torch_squeeze`

), resulting in
the output tensors having 1 fewer dimension than `input`

.

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

Each element of the tensor `input`

is compared with the corresponding
element of the tensor `other`

and an element-wise minimum is taken.
The resulting tensor is returned.

The shapes of `input`

and `other`

don't need to match,
but they must be broadcastable .

$$ \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i) $$

## Examples

```
if (torch_is_installed()) {
a = torch_randn(c(1, 3))
a
torch_min(a)
a = torch_randn(c(4, 4))
a
torch_min(a, dim = 1)
a = torch_randn(c(4))
a
b = torch_randn(c(4))
b
torch_min(a, other = b)
}
#> torch_tensor
#> 0.1125
#> -0.9023
#> -1.0782
#> -0.6685
#> [ CPUFloatType{4} ]
```