# tensorflow::ops::MatrixDiag

`#include <array_ops.h>`

Returns a batched diagonal tensor with a given batched diagonal values.

## Summary

Given a `diagonal`, this operation returns a tensor with the `diagonal` and everything else padded with zeros. The diagonal is computed as follows:

Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:

`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.

For example:

`# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]`

`and diagonal.shape = (2, 4)`

```tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]],
[[5, 0, 0, 0]
[0, 6, 0, 0]
[0, 0, 7, 0]
[0, 0, 0, 8]]]```

```which has shape (2, 4, 4)
```

Arguments:

• scope: A Scope object
• diagonal: Rank `k`, where `k >= 1`.

Returns:

• `Output`: Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.

### Constructors and Destructors

`MatrixDiag(const ::tensorflow::Scope & scope, ::tensorflow::Input diagonal)`

### Public attributes

`operation`
`Operation`
`output`
`::tensorflow::Output`

### Public functions

`node() const `
`::tensorflow::Node *`
`operator::tensorflow::Input() const `
``` ```
``` ```
`operator::tensorflow::Output() const `
``` ```
``` ```

## Public attributes

### operation

`Operation operation`

### output

`::tensorflow::Output output`

## Public functions

### MatrixDiag

``` MatrixDiag(
const ::tensorflow::Scope & scope,
::tensorflow::Input diagonal
)```

### node

`::tensorflow::Node * node() const `

### operator::tensorflow::Input

` operator::tensorflow::Input() const `

### operator::tensorflow::Output

` operator::tensorflow::Output() const `