function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "localResponseNormalization");
    _util.assert($x.rank === 4 || $x.rank === 3, ()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${$x.rank}.`);
    _util.assert(_util.isInt(depthRadius), ()=>`Error in localResponseNormalization: depthRadius must be an ` + `integer but got depthRadius ${depthRadius}.`);
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    const inputs = {
        x: x4D
    };
    const attrs = {
        depthRadius,
        bias,
        alpha,
        beta
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.LRN), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    else return res;
}
Normalizes the activation of a local neighborhood across or within
channels.

@param x The input tensor. The 4-D input tensor is treated as a 3-D array
    of 1D vectors (along the last dimension), and each vector is
    normalized independently.
@param depthRadius The number of adjacent channels in the 1D normalization
    window.
@param bias A constant bias term for the basis.
@param alpha A scale factor, usually positive.
@param beta An exponent.

@doc {heading: 'Operations', subheading: 'Normalization'}