import type { Device, Deviceish } from "./device";
import { Shape, Strides } from "./shape";
import { ATypedArray, Dtype } from "./dtype";
import { TensorArrayData, UntypedStorage } from "./storage";
import type { GradientFunction, GradientContext } from "./autograd";
import type { KernelConfigInput, KernelParamsInput } from "./kernel";
import { TensorBase } from "./tensor_base";
export type TensorData = TensorArrayData | ATypedArray | UntypedStorage;
export type TensorSpec = {
    data: TensorData;
    dtype?: Dtype;
    requiresGrad?: boolean;
    device?: Deviceish;
    shape?: Shape;
    strides?: Strides;
};
export declare class Tensor extends TensorBase {
    private _device;
    private _storage;
    private _dtype;
    private _shape;
    private _strides;
    private _requiresGrad;
    private _gradFunc;
    private _gradCtx;
    grad: Tensor | null;
    get storage(): UntypedStorage;
    get dtype(): Dtype;
    get shape(): Shape;
    get strides(): Strides;
    get device(): Device;
    get isContiguous(): boolean;
    private get isScalar();
    get requiresGrad(): boolean;
    set requiresGrad(value: boolean);
    get gradFunc(): GradientFunction | null;
    constructor(arrayOrSpec: TensorData | TensorSpec, dtype?: Dtype, device?: Deviceish, requiresGrad?: boolean);
    withShape(shape: Shape, strides: Strides): Tensor;
    get [Symbol.toStringTag](): string;
    toString(): string;
    toArrayAsync(): Promise<TensorArrayData>;
    runKernelInplace(name: string, config: KernelConfigInput, params: KernelParamsInput, ...additionalInputs: Tensor[]): Tensor;
    runKernel(name: string, config: KernelConfigInput, params: KernelParamsInput, outputShapes: Shape[], ...additionalInputs: Tensor[]): Tensor[];
    detach(): Tensor;
    setGradientFunction(ctx: GradientContext, gradFunc: GradientFunction): void;
    backward(gradient?: Tensor): void;
    expand(shape: Shape): Tensor;
    mm(other: Tensor): Tensor;
    t(): Tensor;
    zero_(): Tensor;
    /**
    * ![Plot of abs and its gradient](/plots/abs.svg)
    *
    * Calculates:
    * ```js
    * output = abs(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = input == 0 ? 0 : (input > 0 ? outputGrad : -outputGrad)
    * ```
    *
    * @returns the output tensor
    */
    abs(): Tensor;
    /**
    * Alias for `abs`.
    *
    * ![Plot of abs and its gradient](/plots/abs.svg)
    *
    * Calculates:
    * ```js
    * output = abs(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = input == 0 ? 0 : (input > 0 ? outputGrad : -outputGrad)
    * ```
    *
    * @returns the output tensor
    */
    absolute(): Tensor;
    /**
    * ![Plot of abs and its gradient](/plots/abs.svg)
    *
    * Calculates:
    * ```js
    * output = abs(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = input == 0 ? 0 : (input > 0 ? outputGrad : -outputGrad)
    * ```
    *
    * @returns the output tensor
    */
    abs_(): Tensor;
    /**
    * ![Plot of acos and its gradient](/plots/acos.svg)
    *
    * Calculates:
    * ```js
    * output = acos(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad / sqrt(1 - input * input)
    * ```
    *
    * @returns the output tensor
    */
    acos(): Tensor;
    /**
    * Alias for `acos`.
    *
    * ![Plot of acos and its gradient](/plots/acos.svg)
    *
    * Calculates:
    * ```js
    * output = acos(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad / sqrt(1 - input * input)
    * ```
    *
    * @returns the output tensor
    */
    arccos(): Tensor;
    /**
    * ![Plot of acos and its gradient](/plots/acos.svg)
    *
    * Calculates:
    * ```js
    * output = acos(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad / sqrt(1 - input * input)
    * ```
    *
    * @returns the output tensor
    */
    acos_(): Tensor;
    /**
    * ![Plot of acosh and its gradient](/plots/acosh.svg)
    *
    * Calculates:
    * ```js
    * output = acosh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(input * input - 1)
    * ```
    *
    * @returns the output tensor
    */
    acosh(): Tensor;
    /**
    * Alias for `acosh`.
    *
    * ![Plot of acosh and its gradient](/plots/acosh.svg)
    *
    * Calculates:
    * ```js
    * output = acosh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(input * input - 1)
    * ```
    *
    * @returns the output tensor
    */
    arccosh(): Tensor;
    /**
    * ![Plot of acosh and its gradient](/plots/acosh.svg)
    *
    * Calculates:
    * ```js
    * output = acosh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(input * input - 1)
    * ```
    *
    * @returns the output tensor
    */
    acosh_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input + other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad; otherGrad = outputGrad
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    add(other: Tensor, alpha?: number): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input + other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad; otherGrad = outputGrad
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    add_(other: Tensor, alpha?: number): Tensor;
    /**
    * ![Plot of asin and its gradient](/plots/asin.svg)
    *
    * Calculates:
    * ```js
    * output = asin(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(1 - input * input)
    * ```
    *
    * @returns the output tensor
    */
    asin(): Tensor;
    /**
    * Alias for `asin`.
    *
    * ![Plot of asin and its gradient](/plots/asin.svg)
    *
    * Calculates:
    * ```js
    * output = asin(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(1 - input * input)
    * ```
    *
    * @returns the output tensor
    */
    arcsin(): Tensor;
    /**
    * ![Plot of asin and its gradient](/plots/asin.svg)
    *
    * Calculates:
    * ```js
    * output = asin(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(1 - input * input)
    * ```
    *
    * @returns the output tensor
    */
    asin_(): Tensor;
    /**
    * ![Plot of asinh and its gradient](/plots/asinh.svg)
    *
    * Calculates:
    * ```js
    * output = asinh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(input * input + 1)
    * ```
    *
    * @returns the output tensor
    */
    asinh(): Tensor;
    /**
    * Alias for `asinh`.
    *
    * ![Plot of asinh and its gradient](/plots/asinh.svg)
    *
    * Calculates:
    * ```js
    * output = asinh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(input * input + 1)
    * ```
    *
    * @returns the output tensor
    */
    arcsinh(): Tensor;
    /**
    * ![Plot of asinh and its gradient](/plots/asinh.svg)
    *
    * Calculates:
    * ```js
    * output = asinh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / sqrt(input * input + 1)
    * ```
    *
    * @returns the output tensor
    */
    asinh_(): Tensor;
    /**
    * ![Plot of atan and its gradient](/plots/atan.svg)
    *
    * Calculates:
    * ```js
    * output = atan(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input * input + 1)
    * ```
    *
    * @returns the output tensor
    */
    atan(): Tensor;
    /**
    * Alias for `atan`.
    *
    * ![Plot of atan and its gradient](/plots/atan.svg)
    *
    * Calculates:
    * ```js
    * output = atan(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input * input + 1)
    * ```
    *
    * @returns the output tensor
    */
    arctan(): Tensor;
    /**
    * ![Plot of atan and its gradient](/plots/atan.svg)
    *
    * Calculates:
    * ```js
    * output = atan(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input * input + 1)
    * ```
    *
    * @returns the output tensor
    */
    atan_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = atan2(input, other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * other / (input * input + other * other); otherGrad = -outputGrad * input / (input * input + other * other)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    atan2(other: Tensor): Tensor;
    /**
    * Alias for `atan2`.
    *
    * Calculates:
    * ```js
    * output = atan2(input, other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * other / (input * input + other * other); otherGrad = -outputGrad * input / (input * input + other * other)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    arctan2(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = atan2(input, other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * other / (input * input + other * other); otherGrad = -outputGrad * input / (input * input + other * other)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    atan2_(other: Tensor): Tensor;
    /**
    * ![Plot of ceil and its gradient](/plots/ceil.svg)
    *
    * Calculates:
    * ```js
    * output = ceil(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    ceil(): Tensor;
    /**
    * ![Plot of ceil and its gradient](/plots/ceil.svg)
    *
    * Calculates:
    * ```js
    * output = ceil(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    ceil_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = other >= 0 ? abs(input) : -abs(input)
    * ```
    *
    * Gradient:
    * ```js
    * var dir = other >= 0 ? (input >= 0 ? 1.0 : -1.0) : (input >= 0 ? -1.0 : 1.0); inputGrad = input == 0.0 ? 0.0 : outputGrad * dir; otherGrad = 0
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    copysign(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = other >= 0 ? abs(input) : -abs(input)
    * ```
    *
    * Gradient:
    * ```js
    * var dir = other >= 0 ? (input >= 0 ? 1.0 : -1.0) : (input >= 0 ? -1.0 : 1.0); inputGrad = input == 0.0 ? 0.0 : outputGrad * dir; otherGrad = 0
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    copysign_(other: Tensor): Tensor;
    /**
    * ![Plot of cos and its gradient](/plots/cos.svg)
    *
    * Calculates:
    * ```js
    * output = cos(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad * sin(input)
    * ```
    *
    * @returns the output tensor
    */
    cos(): Tensor;
    /**
    * ![Plot of cos and its gradient](/plots/cos.svg)
    *
    * Calculates:
    * ```js
    * output = cos(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad * sin(input)
    * ```
    *
    * @returns the output tensor
    */
    cos_(): Tensor;
    /**
    * ![Plot of cosh and its gradient](/plots/cosh.svg)
    *
    * Calculates:
    * ```js
    * output = cosh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * sinh(input)
    * ```
    *
    * @returns the output tensor
    */
    cosh(): Tensor;
    /**
    * ![Plot of cosh and its gradient](/plots/cosh.svg)
    *
    * Calculates:
    * ```js
    * output = cosh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * sinh(input)
    * ```
    *
    * @returns the output tensor
    */
    cosh_(): Tensor;
    /**
    * ![Plot of deg2rad and its gradient](/plots/deg2rad.svg)
    *
    * Calculates:
    * ```js
    * output = input * 0.017453292519943295
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * 0.017453292519943295
    * ```
    *
    * @returns the output tensor
    */
    deg2rad(): Tensor;
    /**
    * ![Plot of deg2rad and its gradient](/plots/deg2rad.svg)
    *
    * Calculates:
    * ```js
    * output = input * 0.017453292519943295
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * 0.017453292519943295
    * ```
    *
    * @returns the output tensor
    */
    deg2rad_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input / other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / other; otherGrad = -outputGrad * input / (other * other)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    div(other: Tensor, alpha?: number): Tensor;
    /**
    * Alias for `div`.
    *
    * Calculates:
    * ```js
    * output = input / other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / other; otherGrad = -outputGrad * input / (other * other)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    divide(other: Tensor, alpha?: number): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input / other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / other; otherGrad = -outputGrad * input / (other * other)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    div_(other: Tensor, alpha?: number): Tensor;
    /**
    * ![Plot of exp and its gradient](/plots/exp.svg)
    *
    * Calculates:
    * ```js
    * output = exp(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * exp(input)
    * ```
    *
    * @returns the output tensor
    */
    exp(): Tensor;
    /**
    * ![Plot of exp and its gradient](/plots/exp.svg)
    *
    * Calculates:
    * ```js
    * output = exp(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * exp(input)
    * ```
    *
    * @returns the output tensor
    */
    exp_(): Tensor;
    /**
    * ![Plot of exp2 and its gradient](/plots/exp2.svg)
    *
    * Calculates:
    * ```js
    * output = exp2(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * exp2(input) * 0.6931471805599453
    * ```
    *
    * @returns the output tensor
    */
    exp2(): Tensor;
    /**
    * ![Plot of exp2 and its gradient](/plots/exp2.svg)
    *
    * Calculates:
    * ```js
    * output = exp2(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * exp2(input) * 0.6931471805599453
    * ```
    *
    * @returns the output tensor
    */
    exp2_(): Tensor;
    /**
    * ![Plot of expm1 and its gradient](/plots/expm1.svg)
    *
    * Calculates:
    * ```js
    * output = exp(input) - 1.0
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * exp(input)
    * ```
    *
    * @returns the output tensor
    */
    expm1(): Tensor;
    /**
    * ![Plot of expm1 and its gradient](/plots/expm1.svg)
    *
    * Calculates:
    * ```js
    * output = exp(input) - 1.0
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * exp(input)
    * ```
    *
    * @returns the output tensor
    */
    expm1_(): Tensor;
    /**
    * ![Plot of floor and its gradient](/plots/floor.svg)
    *
    * Calculates:
    * ```js
    * output = floor(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    floor(): Tensor;
    /**
    * ![Plot of floor and its gradient](/plots/floor.svg)
    *
    * Calculates:
    * ```js
    * output = floor(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    floor_(): Tensor;
    /**
    * ![Plot of frac and its gradient](/plots/frac.svg)
    *
    * Calculates:
    * ```js
    * output = input >= 0.0 ? fract(input) : -fract(-input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad
    * ```
    *
    * @returns the output tensor
    */
    frac(): Tensor;
    /**
    * ![Plot of frac and its gradient](/plots/frac.svg)
    *
    * Calculates:
    * ```js
    * output = input >= 0.0 ? fract(input) : -fract(-input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad
    * ```
    *
    * @returns the output tensor
    */
    frac_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = sqrt(input * input + other * other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * input / sqrt(input * input + other * other); otherGrad = outputGrad * other / sqrt(input * input + other * other)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    hypot(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = sqrt(input * input + other * other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * input / sqrt(input * input + other * other); otherGrad = outputGrad * other / sqrt(input * input + other * other)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    hypot_(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input * pow(2.0, other)
    * ```
    *
    * Gradient:
    * ```js
    * var out = pow(2.0, other); inputGrad = outputGrad * out; otherGrad = outputGrad * input * out * 0.6931471805599453
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    ldexp(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input * pow(2.0, other)
    * ```
    *
    * Gradient:
    * ```js
    * var out = pow(2.0, other); inputGrad = outputGrad * out; otherGrad = outputGrad * input * out * 0.6931471805599453
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    ldexp_(other: Tensor): Tensor;
    /**
    * ![Plot of log and its gradient](/plots/log.svg)
    *
    * Calculates:
    * ```js
    * output = log(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / input
    * ```
    *
    * @returns the output tensor
    */
    log(): Tensor;
    /**
    * ![Plot of log and its gradient](/plots/log.svg)
    *
    * Calculates:
    * ```js
    * output = log(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / input
    * ```
    *
    * @returns the output tensor
    */
    log_(): Tensor;
    /**
    * ![Plot of log10 and its gradient](/plots/log10.svg)
    *
    * Calculates:
    * ```js
    * output = log(input) * 0.4342944819032518
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input * 2.302585092994046)
    * ```
    *
    * @returns the output tensor
    */
    log10(): Tensor;
    /**
    * ![Plot of log10 and its gradient](/plots/log10.svg)
    *
    * Calculates:
    * ```js
    * output = log(input) * 0.4342944819032518
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input * 2.302585092994046)
    * ```
    *
    * @returns the output tensor
    */
    log10_(): Tensor;
    /**
    * ![Plot of log1p and its gradient](/plots/log1p.svg)
    *
    * Calculates:
    * ```js
    * output = log(input + 1.0)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input + 1.0)
    * ```
    *
    * @returns the output tensor
    */
    log1p(): Tensor;
    /**
    * ![Plot of log1p and its gradient](/plots/log1p.svg)
    *
    * Calculates:
    * ```js
    * output = log(input + 1.0)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input + 1.0)
    * ```
    *
    * @returns the output tensor
    */
    log1p_(): Tensor;
    /**
    * ![Plot of log2 and its gradient](/plots/log2.svg)
    *
    * Calculates:
    * ```js
    * output = log2(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input * 0.6931471805599453)
    * ```
    *
    * @returns the output tensor
    */
    log2(): Tensor;
    /**
    * ![Plot of log2 and its gradient](/plots/log2.svg)
    *
    * Calculates:
    * ```js
    * output = log2(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (input * 0.6931471805599453)
    * ```
    *
    * @returns the output tensor
    */
    log2_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = log(exp(input) + exp(other))
    * ```
    *
    * Gradient:
    * ```js
    * var ein = exp(input); var eoth = exp(other); var addeinv = outputGrad/(ein + eoth); inputGrad = addeinv * ein; otherGrad = addeinv * eoth
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    logaddexp(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = log(exp(input) + exp(other))
    * ```
    *
    * Gradient:
    * ```js
    * var ein = exp(input); var eoth = exp(other); var addeinv = outputGrad/(ein + eoth); inputGrad = addeinv * ein; otherGrad = addeinv * eoth
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    logaddexp_(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = log2(exp2(input) + exp2(other))
    * ```
    *
    * Gradient:
    * ```js
    * var ein = exp2(input); var eoth = exp2(other); var sum_ein_eoth = ein + eoth; inputGrad = outputGrad * (ein / sum_ein_eoth); otherGrad = outputGrad * (eoth / sum_ein_eoth );
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    logaddexp2(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = log2(exp2(input) + exp2(other))
    * ```
    *
    * Gradient:
    * ```js
    * var ein = exp2(input); var eoth = exp2(other); var sum_ein_eoth = ein + eoth; inputGrad = outputGrad * (ein / sum_ein_eoth); otherGrad = outputGrad * (eoth / sum_ein_eoth );
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    logaddexp2_(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input * other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * other; otherGrad = outputGrad * input
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    mul(other: Tensor, alpha?: number): Tensor;
    /**
    * Alias for `mul`.
    *
    * Calculates:
    * ```js
    * output = input * other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * other; otherGrad = outputGrad * input
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    multiply(other: Tensor, alpha?: number): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input * other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * other; otherGrad = outputGrad * input
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    mul_(other: Tensor, alpha?: number): Tensor;
    /**
    * ![Plot of neg and its gradient](/plots/neg.svg)
    *
    * Calculates:
    * ```js
    * output = -input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad
    * ```
    *
    * @returns the output tensor
    */
    neg(): Tensor;
    /**
    * Alias for `neg`.
    *
    * ![Plot of neg and its gradient](/plots/neg.svg)
    *
    * Calculates:
    * ```js
    * output = -input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad
    * ```
    *
    * @returns the output tensor
    */
    negative(): Tensor;
    /**
    * ![Plot of neg and its gradient](/plots/neg.svg)
    *
    * Calculates:
    * ```js
    * output = -input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad
    * ```
    *
    * @returns the output tensor
    */
    neg_(): Tensor;
    /**
    * ![Plot of positive and its gradient](/plots/positive.svg)
    *
    * Calculates:
    * ```js
    * output = input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad
    * ```
    *
    * @returns the output tensor
    */
    positive(): Tensor;
    /**
    * ![Plot of positive and its gradient](/plots/positive.svg)
    *
    * Calculates:
    * ```js
    * output = input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad
    * ```
    *
    * @returns the output tensor
    */
    positive_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = pow(input, other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * other * pow(input, other - 1.0); otherGrad = outputGrad * pow(input, other) * log(input)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    pow(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = pow(input, other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * other * pow(input, other - 1.0); otherGrad = outputGrad * pow(input, other) * log(input)
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    pow_(other: Tensor): Tensor;
    /**
    * ![Plot of rad2deg and its gradient](/plots/rad2deg.svg)
    *
    * Calculates:
    * ```js
    * output = input * 57.29577951308232
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * 57.29577951308232
    * ```
    *
    * @returns the output tensor
    */
    rad2deg(): Tensor;
    /**
    * ![Plot of rad2deg and its gradient](/plots/rad2deg.svg)
    *
    * Calculates:
    * ```js
    * output = input * 57.29577951308232
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * 57.29577951308232
    * ```
    *
    * @returns the output tensor
    */
    rad2deg_(): Tensor;
    /**
    * ![Plot of reciprocal and its gradient](/plots/reciprocal.svg)
    *
    * Calculates:
    * ```js
    * output = 1.0 / input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad / (input * input)
    * ```
    *
    * @returns the output tensor
    */
    reciprocal(): Tensor;
    /**
    * ![Plot of reciprocal and its gradient](/plots/reciprocal.svg)
    *
    * Calculates:
    * ```js
    * output = 1.0 / input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad / (input * input)
    * ```
    *
    * @returns the output tensor
    */
    reciprocal_(): Tensor;
    /**
    * ![Plot of relu and its gradient](/plots/relu.svg)
    *
    * Calculates:
    * ```js
    * output = max(input, 0.0)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = input > 0.0 ? outputGrad : 0.0
    * ```
    *
    * @returns the output tensor
    */
    relu(): Tensor;
    /**
    * ![Plot of relu and its gradient](/plots/relu.svg)
    *
    * Calculates:
    * ```js
    * output = max(input, 0.0)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = input > 0.0 ? outputGrad : 0.0
    * ```
    *
    * @returns the output tensor
    */
    relu_(): Tensor;
    /**
    * ![Plot of round and its gradient](/plots/round.svg)
    *
    * Calculates:
    * ```js
    * output = round(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    round(): Tensor;
    /**
    * ![Plot of round and its gradient](/plots/round.svg)
    *
    * Calculates:
    * ```js
    * output = round(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    round_(): Tensor;
    /**
    * ![Plot of rsqrt and its gradient](/plots/rsqrt.svg)
    *
    * Calculates:
    * ```js
    * output = 1.0 / sqrt(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad / (2.0 * sqrt(input) * input)
    * ```
    *
    * @returns the output tensor
    */
    rsqrt(): Tensor;
    /**
    * ![Plot of rsqrt and its gradient](/plots/rsqrt.svg)
    *
    * Calculates:
    * ```js
    * output = 1.0 / sqrt(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = -outputGrad / (2.0 * sqrt(input) * input)
    * ```
    *
    * @returns the output tensor
    */
    rsqrt_(): Tensor;
    /**
    * ![Plot of sigmoid and its gradient](/plots/sigmoid.svg)
    *
    * Calculates:
    * ```js
    * output = 1.0 / (1.0 + exp(-input))
    * ```
    *
    * Gradient:
    * ```js
    * var out = 1.0 / (1.0 + exp(-input)); inputGrad = outputGrad * out * (1.0 - out)
    * ```
    *
    * @returns the output tensor
    */
    sigmoid(): Tensor;
    /**
    * ![Plot of sigmoid and its gradient](/plots/sigmoid.svg)
    *
    * Calculates:
    * ```js
    * output = 1.0 / (1.0 + exp(-input))
    * ```
    *
    * Gradient:
    * ```js
    * var out = 1.0 / (1.0 + exp(-input)); inputGrad = outputGrad * out * (1.0 - out)
    * ```
    *
    * @returns the output tensor
    */
    sigmoid_(): Tensor;
    /**
    * ![Plot of sign and its gradient](/plots/sign.svg)
    *
    * Calculates:
    * ```js
    * output = sign(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    sign(): Tensor;
    /**
    * ![Plot of sign and its gradient](/plots/sign.svg)
    *
    * Calculates:
    * ```js
    * output = sign(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    sign_(): Tensor;
    /**
    * ![Plot of silu and its gradient](/plots/silu.svg)
    *
    * Calculates:
    * ```js
    * output = input / (1.0 + exp(-input))
    * ```
    *
    * Gradient:
    * ```js
    * var out = 1.0 / (1.0 + exp(-input)); inputGrad = outputGrad * (out + input * out * (1.0 - out))
    * ```
    *
    * @returns the output tensor
    */
    silu(): Tensor;
    /**
    * ![Plot of silu and its gradient](/plots/silu.svg)
    *
    * Calculates:
    * ```js
    * output = input / (1.0 + exp(-input))
    * ```
    *
    * Gradient:
    * ```js
    * var out = 1.0 / (1.0 + exp(-input)); inputGrad = outputGrad * (out + input * out * (1.0 - out))
    * ```
    *
    * @returns the output tensor
    */
    silu_(): Tensor;
    /**
    * ![Plot of sin and its gradient](/plots/sin.svg)
    *
    * Calculates:
    * ```js
    * output = sin(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * cos(input)
    * ```
    *
    * @returns the output tensor
    */
    sin(): Tensor;
    /**
    * ![Plot of sin and its gradient](/plots/sin.svg)
    *
    * Calculates:
    * ```js
    * output = sin(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * cos(input)
    * ```
    *
    * @returns the output tensor
    */
    sin_(): Tensor;
    /**
    * ![Plot of sinc and its gradient](/plots/sinc.svg)
    *
    * Calculates:
    * ```js
    * var inpi = input * 3.141592653589793; output = input == 0.0 ? 1.0 : sin(inpi) / inpi
    * ```
    *
    * Gradient:
    * ```js
    * var inpi = input * 3.141592653589793; inputGrad = input == 0.0 ? 0.0 : (outputGrad * 3.141592653589793 * (inpi*cos(inpi) - sin(inpi)) / (inpi*inpi))
    * ```
    *
    * @returns the output tensor
    */
    sinc(): Tensor;
    /**
    * ![Plot of sinc and its gradient](/plots/sinc.svg)
    *
    * Calculates:
    * ```js
    * var inpi = input * 3.141592653589793; output = input == 0.0 ? 1.0 : sin(inpi) / inpi
    * ```
    *
    * Gradient:
    * ```js
    * var inpi = input * 3.141592653589793; inputGrad = input == 0.0 ? 0.0 : (outputGrad * 3.141592653589793 * (inpi*cos(inpi) - sin(inpi)) / (inpi*inpi))
    * ```
    *
    * @returns the output tensor
    */
    sinc_(): Tensor;
    /**
    * ![Plot of sinh and its gradient](/plots/sinh.svg)
    *
    * Calculates:
    * ```js
    * output = sinh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * cosh(input)
    * ```
    *
    * @returns the output tensor
    */
    sinh(): Tensor;
    /**
    * ![Plot of sinh and its gradient](/plots/sinh.svg)
    *
    * Calculates:
    * ```js
    * output = sinh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * cosh(input)
    * ```
    *
    * @returns the output tensor
    */
    sinh_(): Tensor;
    /**
    * ![Plot of sqrt and its gradient](/plots/sqrt.svg)
    *
    * Calculates:
    * ```js
    * output = sqrt(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (2.0 * sqrt(input))
    * ```
    *
    * @returns the output tensor
    */
    sqrt(): Tensor;
    /**
    * ![Plot of sqrt and its gradient](/plots/sqrt.svg)
    *
    * Calculates:
    * ```js
    * output = sqrt(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (2.0 * sqrt(input))
    * ```
    *
    * @returns the output tensor
    */
    sqrt_(): Tensor;
    /**
    * ![Plot of square and its gradient](/plots/square.svg)
    *
    * Calculates:
    * ```js
    * output = input * input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * 2.0 * input
    * ```
    *
    * @returns the output tensor
    */
    square(): Tensor;
    /**
    * ![Plot of square and its gradient](/plots/square.svg)
    *
    * Calculates:
    * ```js
    * output = input * input
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * 2.0 * input
    * ```
    *
    * @returns the output tensor
    */
    square_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input - other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad; otherGrad = -outputGrad
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    sub(other: Tensor, alpha?: number): Tensor;
    /**
    * Alias for `sub`.
    *
    * Calculates:
    * ```js
    * output = input - other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad; otherGrad = -outputGrad
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    subtract(other: Tensor, alpha?: number): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input - other
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad; otherGrad = -outputGrad
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @param alpha the alpha value to multiply `other` with
    * @returns the output tensor
    */
    sub_(other: Tensor, alpha?: number): Tensor;
    /**
    * ![Plot of tan and its gradient](/plots/tan.svg)
    *
    * Calculates:
    * ```js
    * output = tan(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (cos(input) * cos(input))
    * ```
    *
    * @returns the output tensor
    */
    tan(): Tensor;
    /**
    * ![Plot of tan and its gradient](/plots/tan.svg)
    *
    * Calculates:
    * ```js
    * output = tan(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / (cos(input) * cos(input))
    * ```
    *
    * @returns the output tensor
    */
    tan_(): Tensor;
    /**
    * ![Plot of tanh and its gradient](/plots/tanh.svg)
    *
    * Calculates:
    * ```js
    * output = tanh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * (1.0 - tanh(input) * tanh(input))
    * ```
    *
    * @returns the output tensor
    */
    tanh(): Tensor;
    /**
    * ![Plot of tanh and its gradient](/plots/tanh.svg)
    *
    * Calculates:
    * ```js
    * output = tanh(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * (1.0 - tanh(input) * tanh(input))
    * ```
    *
    * @returns the output tensor
    */
    tanh_(): Tensor;
    /**
    * ![Plot of trunc and its gradient](/plots/trunc.svg)
    *
    * Calculates:
    * ```js
    * output = trunc(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    trunc(): Tensor;
    /**
    * Alias for `trunc`.
    *
    * ![Plot of trunc and its gradient](/plots/trunc.svg)
    *
    * Calculates:
    * ```js
    * output = trunc(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    fix(): Tensor;
    /**
    * ![Plot of trunc and its gradient](/plots/trunc.svg)
    *
    * Calculates:
    * ```js
    * output = trunc(input)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = 0
    * ```
    *
    * @returns the output tensor
    */
    trunc_(): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input == 0.0 ? 0.0 : input * log(other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = input == 0.0 ? 0.0 : outputGrad * log(other); otherGrad = input == 0.0 ? 0.0 : outputGrad * (input / other);
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    xlogy(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = input == 0.0 ? 0.0 : input * log(other)
    * ```
    *
    * Gradient:
    * ```js
    * inputGrad = input == 0.0 ? 0.0 : outputGrad * log(other); otherGrad = input == 0.0 ? 0.0 : outputGrad * (input / other);
    * ```
    *
    * @param other the other tensor whose shape is broadcastable with the input tensor
    * @returns the output tensor
    */
    xlogy_(other: Tensor): Tensor;
    /**
    * Calculates:
    * ```js
    * output = output && input
    * ```
    *
    * with an initial value of `output = 1`.
    *
    * Gradient:
    * ```js
    * inputGrad = output ? outputGrad : 0.0
    * ```
    *
    * @returns the output tensor
    */
    all(dim?: number, keepdim?: boolean): Tensor;
    /**
    * Calculates:
    * ```js
    * output = output || input
    * ```
    *
    * with an initial value of `output = 0`.
    *
    * Gradient:
    * ```js
    * inputGrad = output ? outputGrad : 0.0
    * ```
    *
    * @returns the output tensor
    */
    any(dim?: number, keepdim?: boolean): Tensor;
    /**
    * Calculates:
    * ```js
    * output = output + input
    * ```
    *
    * with an initial value of `output = 0.0`.
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad / inputSize
    * ```
    *
    * @returns the output tensor
    */
    mean(dim?: number, keepdim?: boolean): Tensor;
    /**
    * Calculates:
    * ```js
    * output = output + input * input
    * ```
    *
    * with an initial value of `output = 0.0`.
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * input / output
    * ```
    *
    * @returns the output tensor
    */
    norm(dim?: number, keepdim?: boolean): Tensor;
    /**
    * Calculates:
    * ```js
    * output = output * input
    * ```
    *
    * with an initial value of `output = 1.0`.
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad * output / input
    * ```
    *
    * @returns the output tensor
    */
    prod(dim?: number, keepdim?: boolean): Tensor;
    /**
    * Calculates:
    * ```js
    * output = output + input
    * ```
    *
    * with an initial value of `output = 0.0`.
    *
    * Gradient:
    * ```js
    * inputGrad = outputGrad
    * ```
    *
    * @returns the output tensor
    */
    sum(dim?: number, keepdim?: boolean): Tensor;
    /**
    * Calculates:
    * ```js
    * output = output + (input != 0)
    * ```
    *
    * with an initial value of `output = 0.0`.
    *
    * @returns the output tensor
    */
    countNonzero(dim?: number, keepdim?: boolean): Tensor;
}