Warning: This API is deprecated and will be removed in a future version of TensorFlow after the replacement is stable.

# SpaceToBatchNd

public final class SpaceToBatchNd

SpaceToBatch for N-D tensors of type T.

This operation divides "spatial" dimensions `[1, ..., M]` of the input into a grid of blocks of shape `block_shape`, and interleaves these blocks with the "batch" dimension (0) such that in the output, the spatial dimensions `[1, ..., M]` correspond to the position within the grid, and the batch dimension combines both the position within a spatial block and the original batch position. Prior to division into blocks, the spatial dimensions of the input are optionally zero padded according to `paddings`. See below for a precise description.

This operation is equivalent to the following steps:

1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input according to `paddings` to produce `padded` of shape `padded_shape`.

[batch] + [padded_shape / block_shape, block_shape, ..., padded_shape[M] / block_shape[M-1], block_shape[M-1]] + remaining_shape

block_shape + [batch] + [padded_shape / block_shape, ..., padded_shape[M] / block_shape[M-1]] + remaining_shape

4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch dimension, producing an output tensor of shape:

[batch * prod(block_shape)] + [padded_shape / block_shape, ..., padded_shape[M] / block_shape[M-1]] + remaining_shape

Some examples:

(1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`:

``````x = [[[, ], [, ]]]
``````
The output tensor has shape `[4, 1, 1, 1]` and value:
``````[[[]], [[]], [[]], [[]]]
``````
(2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`:
``````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]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`:
``````x = [[[,   ,  ,  ],
[,   ,  ,  ],
[,  , ,  ],
[, , ,  ]]]
``````
The output tensor has shape `[4, 2, 2, 1]` and value:
``````x = [[[, ], [, ]],
[[, ], [, ]],
[[, ], [, ]],
[[, ], [, ]]]
``````
(4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and paddings = `[[0, 0], [2, 0]]`:
``````x = [[[,   ,  ,  ],
[,   ,  ,  ]],
[[,  , ,  ],
[, , ,  ]]]
``````
The output tensor has shape `[8, 1, 3, 1]` and value:
``````x = [[[, , ]], [[, , ]],
[[, , ]], [[, , ]],
[[, , ]], [[, , ]],
[[, , ]], [[, , ]]]
``````
Among others, this operation is useful for reducing atrous convolution into regular convolution.

### Public Methods

 Output asOutput() Returns the symbolic handle of a tensor. static SpaceToBatchNd create(Scope scope, Operand input, Operand blockShape, Operand paddings) Factory method to create a class wrapping a new SpaceToBatchNd operation. Output output()

## Public Methods

#### public Output<T> asOutput()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

#### public static SpaceToBatchNd<T> create(Scope scope, Operand<T> input, Operand<U> blockShape, Operand<V> paddings)

Factory method to create a class wrapping a new SpaceToBatchNd operation.

##### Parameters
scope current scope N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, where spatial_shape has `M` dimensions. 1-D with shape `[M]`, all values must be >= 1. 2-D with shape `[M, 2]`, all values must be >= 0. `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension `i + 1`, which corresponds to spatial dimension `i`. It is required that `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`.
##### Returns
• a new instance of SpaceToBatchNd

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