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# tensorflow::ops::BatchToSpace

`#include <array_ops.h>`

BatchToSpace for 4-D tensors of type T.

## Summary

This is a legacy version of the more general BatchToSpaceND.

Rearranges (permutes) data from batch into blocks of spatial data, followed by cropping. This is the reverse transformation of SpaceToBatch. More specifically, this op outputs a copy of the input tensor where values from the `batch` dimension are moved in spatial blocks to the `height` and `width` dimensions, followed by cropping along the `height` and `width` dimensions.

Arguments:

• scope: A Scope object
• input: 4-D tensor with shape `[batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth]`. Note that the batch size of the input tensor must be divisible by `block_size * block_size`.
• crops: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies how many elements to crop from the intermediate result across the spatial dimensions as follows:
```crops = [[crop_top, crop_bottom], [crop_left, crop_right]]
```

Returns:

• `Output`: 4-D with shape `[batch, height, width, depth]`, where:
```height = height_pad - crop_top - crop_bottom
width = width_pad - crop_left - crop_right
```

The attr `block_size` must be greater than one. It indicates the block size.

Some examples:

(1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2:

```[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
```

The output tensor has shape `[1, 2, 2, 1]` and value:

```x = [[[[1], [2]], [[3], [4]]]]
```

(2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2:

```[[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]
```

The output tensor has shape `[1, 2, 2, 3]` and value:

```x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
```

(3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2:

```x = [[[[1], [3]], [[9], [11]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]]
```

The output tensor has shape `[1, 4, 4, 1]` and value:

```x = [[[[1],   [2],  [3],  [4]],
[[5],   [6],  [7],  [8]],
[[9],  [10], [11],  [12]],
[[13], [14], [15],  [16]]]]
```

(4) For the following input of shape `[8, 1, 2, 1]` and block_size of 2:

```x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
[[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
```

The output tensor has shape `[2, 2, 4, 1]` and value:

```x = [[[[1], [3]], [[5], [7]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]]
```

### Constructors and Destructors

`BatchToSpace(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input crops, int64 block_size)`

### 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

### BatchToSpace

``` BatchToSpace(
const ::tensorflow::Scope & scope,
::tensorflow::Input input,
::tensorflow::Input crops,
int64 block_size
)```

### node

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

### operator::tensorflow::Input

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

### operator::tensorflow::Output

` operator::tensorflow::Output() const `
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]