Function tensorScatterUpdate_

tfjs-core/src/ops/tensor_scatter_update.ts:60–85

* Creates a new tensor by applying sparse updates to individual * values or slices to the passed in tensor according to * indices. This operator is the similar to scatterNd op, except that the * udpates are scattered on an existing tensor (as opposed to a zero-tensor). * * If indices contains d

(
    tensor: Tensor<R>|TensorLike, indices: Tensor|TensorLike,
    updates: Tensor|TensorLike)

 * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
 */
function tensorScatterUpdate_<R extends Rank>(
    tensor: Tensor<R>|TensorLike, indices: Tensor|TensorLike,
    updates: Tensor|TensorLike): Tensor<R> {
  const $tensor = convertToTensor(tensor, 'tensor', 'tensorScatterupdate');
  const $indices =
      convertToTensor(indices, 'indices', 'tensorScatterupdate', 'int32');
  const $updates = convertToTensor(updates, 'updates', 'tensorScatterupdate');
  scatter_nd_util.validateInput($updates, $indices, $tensor.shape);
  if ($tensor.dtype !== $updates.dtype) {
    throw new Error(
        `tensor and updates must have the same dtype, instead they are ${
            $tensor.dtype} and ${$updates.dtype}.`);
  }

  const inputs: TensorScatterUpdateInputs = {
    tensor: $tensor,
    indices: $indices,
    updates: $updates
  };
  const attrs: TensorScatterUpdateAttrs = {};

  // tslint:disable-next-line: no-unnecessary-type-assertion
  return ENGINE.runKernel(
             TensorScatterUpdate, inputs as unknown as NamedTensorMap,
             attrs as unknown as NamedAttrMap) as Tensor<R>;
}

export const tensorScatterUpdate = op({tensorScatterUpdate_});