### `tf.space_to_batch_nd(input, block_shape, paddings, name=None)` {#space_to_batch_nd}

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.

##### Args:


*  <b>`input`</b>: A `Tensor`.
    N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
    where spatial_shape has `M` dimensions.
*  <b>`block_shape`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
    1-D with shape `[M]`, all values must be >= 1.
*  <b>`paddings`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
    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`.

    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`.

    2. Reshape `padded` to `reshaped_padded` of shape:

         [batch] +
         [padded_shape[1] / block_shape[0],
           block_shape[0],
          ...,
          padded_shape[M] / block_shape[M-1],
          block_shape[M-1]] +
         remaining_shape

    3. Permute dimensions of `reshaped_padded` to produce
       `permuted_reshaped_padded` of shape:

         block_shape +
         [batch] +
         [padded_shape[1] / block_shape[0],
          ...,
          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[1] / block_shape[0],
          ...,
          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]]`:

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

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

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

    (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and
        `paddings = [[0, 0], [0, 0]]`:

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

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

    ```prettyprint
    [[[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]]`:

    ```prettyprint
    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:

    ```prettyprint
    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]`, block_shape = `[2, 2]`, and
        paddings = `[[0, 0], [2, 0]]`:

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

    The output tensor has shape `[8, 1, 3, 1]` and value:

    ```prettyprint
    x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
         [[[0], [2], [4]]], [[[0], [10], [12]]],
         [[[0], [5], [7]]], [[[0], [13], [15]]],
         [[[0], [6], [8]]], [[[0], [14], [16]]]]
    ```

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

*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  A `Tensor`. Has the same type as `input`.