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### `tf.batch_to_space(input, crops, block_size, name=None)` {#batch_to_space}
BatchToSpace for 4-D tensors of type T.
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.
##### Args:
* <b>`input`</b>: A `Tensor`. 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`.
* <b>`crops`</b>: A `Tensor` of type `int32`.
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]]
* <b>`block_size`</b>: An `int`.
* <b>`name`</b>: A name for the operation (optional).
##### Returns:
A `Tensor`. Has the same type as `input`.
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.
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