Function gather_nd

tensorflow/python/ops/array_ops.py:4130–4291

r"""Gather slices from `params` into a Tensor with shape specified by `indices`. `indices` is an K-dimensional integer tensor, best thought of as a (K-1)-dimensional tensor of indices into `params`, where each element defines a slice of `params`: output[\\(i_0, ..., i_{K-2}\\)] = param

(params, indices, name=None, batch_dims=0)

@dispatch.add_dispatch_support
@deprecated_endpoints("manip.gather_nd")
def gather_nd(params, indices, name=None, batch_dims=0):
  r"""Gather slices from `params` into a Tensor with shape specified by `indices`.

  `indices` is an K-dimensional integer tensor, best thought of as a
  (K-1)-dimensional tensor of indices into `params`, where each element defines
  a slice of `params`:

      output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]]

  Whereas in `tf.gather` `indices` defines slices into the first
  dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the
  first `N` dimensions of `params`, where `N = indices.shape[-1]`.

  The last dimension of `indices` can be at most the rank of
  `params`:

      indices.shape[-1] <= params.rank

  The last dimension of `indices` corresponds to elements
  (if `indices.shape[-1] == params.rank`) or slices
  (if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`
  of `params`.  The output tensor has shape

      indices.shape[:-1] + params.shape[indices.shape[-1]:]

  Additionally both 'params' and 'indices' can have M leading batch
  dimensions that exactly match. In this case 'batch_dims' must be M.

  Note that on CPU, if an out of bound index is found, an error is returned.
  On GPU, if an out of bound index is found, a 0 is stored in the
  corresponding output value.

  Some examples below.

  Simple indexing into a matrix:

  ```python
      indices = [[0, 0], [1, 1]]
      params = [['a', 'b'], ['c', 'd']]
      output = ['a', 'd']
  ```

  Slice indexing into a matrix:

  ```python
      indices = [[1], [0]]
      params = [['a', 'b'], ['c', 'd']]
      output = [['c', 'd'], ['a', 'b']]
  ```

  Indexing into a 3-tensor:

  ```python
      indices = [[1]]
      params = [[['a0', 'b0'], ['c0', 'd0']],
                [['a1', 'b1'], ['c1', 'd1']]]
      output = [[['a1', 'b1'], ['c1', 'd1']]]