# tf.raw_ops.SpaceToBatch

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SpaceToBatch for 4-D tensors of type T.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.SpaceToBatch`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SpaceToBatch)

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    tf.raw_ops.SpaceToBatch(
        input, paddings, block_size, name=None
    )

This is a legacy version of the more general SpaceToBatchND.

Zero-pads and then rearranges (permutes) blocks of spatial data into batch.
More specifically, this op outputs a copy of the input tensor where values from
the `height` and `width` dimensions are moved to the `batch` dimension. After
the zero-padding, both `height` and `width` of the input must be divisible by the
block size.

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

- Non-overlapping blocks of size `block_size x block size` in the height and width dimensions are rearranged into the batch dimension at each location.
- The batch of the output tensor is `batch * block_size * block_size`.
- Both height_pad and width_pad must be divisible by block_size.

The shape of the output will be:

    [batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
     depth]

#### Some examples:

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

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

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

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

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

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

The output tensor has shape `[4, 1, 1, 3]` and value:

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

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

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

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

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

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

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

The output tensor has shape `[8, 1, 2, 1]` and value:

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

Among others, this operation is useful for reducing atrous convolution into
regular convolution.

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| ## Args ||
|---|---|
| `input` | A `Tensor`. 4-D with shape `[batch, height, width, depth]`. |
| `paddings` | A `Tensor`. Must be one of the following types: `int32`, `int64`. 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies the padding of the input with zeros across the spatial dimensions as follows: <br /> paddings = [[pad_top, pad_bottom], [pad_left, pad_right]] The effective spatial dimensions of the zero-padded input tensor will be: height_pad = pad_top + height + pad_bottom width_pad = pad_left + width + pad_right <br /> |
| `block_size` | An `int` that is `>= 2`. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `input`. ||

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