import { Tensor } from '../tensor';
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import { TensorLike } from '../types';
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/**
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* Gather slices from input tensor into a Tensor with shape specified by
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* `indices`.
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*
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* `indices` is an K-dimensional integer tensor, best thought of as a
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* (K-1)-dimensional tensor of indices into input, where each element defines a
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* slice of input:
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* output[\\(i_0, ..., i_{K-2}\\)] = input[indices[\\(i_0, ..., i_{K-2}\\)]]
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*
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* Whereas in `tf.gather`, `indices` defines slices into the first dimension of
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* input, in `tf.gatherND`, `indices` defines slices into the first N dimensions
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* of input, where N = indices.shape[-1].
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*
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* The last dimension of indices can be at most the rank of input:
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* indices.shape[-1] <= input.rank
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*
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* The last dimension of `indices` corresponds to elements
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* (if indices.shape[-1] == input.rank) or slices
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* (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of
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* input.
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* The output tensor has shape
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* indices.shape[:-1] + input.shape[indices.shape[-1]:]
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*
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* Note that on CPU, if an out of bound index is found, an error is returned. On
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* GPU, if an out of bound index is found, a 0 is stored in the corresponding
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* output value.
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*
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* ```js
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* const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');
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* const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);
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* tf.gatherND(input, indices).print() // [10, 11]
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* ```
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*
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* @param x The tensor from which to gather values.
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* @param indices Index tensor, must be of type int32.
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*/
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/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */
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declare function gatherND_(x: Tensor | TensorLike, indices: Tensor | TensorLike): Tensor;
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export declare const gatherND: typeof gatherND_;
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export {};
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