@tensorflow/tfjs-core/dist/ops/gather_nd.d.ts

Hardware-accelerated JavaScript library for machine intelligence

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