# tensorplay.functional.diag

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.diag.html

```python
tensorplay.functional.diag(input, diagonal=0, *, out=None) → Tensor
```

- If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.

- If input is a matrix (2-D tensor), then returns a 1-D tensor with the diagonal elements of input.

The argument [diagonal](/docs/generated/tensorplay.functional.diagonal.html#tensorplay.functional.diagonal) controls which diagonal to consider:

- If [diagonal](/docs/generated/tensorplay.functional.diagonal.html#tensorplay.functional.diagonal) = 0, it is the main diagonal.

- If [diagonal](/docs/generated/tensorplay.functional.diagonal.html#tensorplay.functional.diagonal) > 0, it is above the main diagonal.

- If [diagonal](/docs/generated/tensorplay.functional.diagonal.html#tensorplay.functional.diagonal) 
Parameters:

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

- diagonal ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – the diagonal to consider

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

> **See also**
>
> tensorplay.diagonal() always returns the diagonal of its input. tensorplay.diagflat() always constructs a tensor with diagonal elements specified by the input.

Examples:

Get the square matrix where the input vector is the diagonal:

```
>>> a = tensorplay.randn(3)
>>> a
tensor([ 0.5950,-0.0872, 2.3298])
>>> tensorplay.diag(a)
tensor([[ 0.5950, 0.0000, 0.0000],
        [ 0.0000,-0.0872, 0.0000],
        [ 0.0000, 0.0000, 2.3298]])
>>> tensorplay.diag(a, 1)
tensor([[ 0.0000, 0.5950, 0.0000, 0.0000],
        [ 0.0000, 0.0000,-0.0872, 0.0000],
        [ 0.0000, 0.0000, 0.0000, 2.3298],
        [ 0.0000, 0.0000, 0.0000, 0.0000]])
```

Get the k-th diagonal of a given matrix:

```
>>> a = tensorplay.randn(3, 3)
>>> a
tensor([[-0.4264, 0.0255,-0.1064],
        [ 0.8795,-0.2429, 0.1374],
        [ 0.1029,-0.6482,-1.6300]])
>>> tensorplay.diag(a, 0)
tensor([-0.4264,-0.2429,-1.6300])
>>> tensorplay.diag(a, 1)
tensor([ 0.0255, 0.1374])
```