import Tensor, { Activation, DType, PadMode, TensorValues } from '../../types'; export declare class CPUTensor extends Tensor { static range(start: number, limit: number, delta: number): CPUTensor<"float32">; /** * Array of values of the tensor in contiguous layout */ values: TensorValues[DTpe]; /** * Shape of the tensor */ shape: ReadonlyArray; /** * Strides for all dimensions, ie. the step size per dimension in the contiguous layout */ strides: ReadonlyArray; /** * Total number of entries in the tensor */ size: number; /** * If this tensor was already deleted */ deleted: boolean; constructor(shape: ReadonlyArray, values?: TensorValues[DTpe] | number[], dtype?: DTpe); getValues(): Promise; getShape(): readonly number[]; constantLike(value: number): Tensor; singleConstant(value: number): Tensor; cast(dtype: DTpe2): Tensor; delete(): void; copy(newShape?: number[]): Tensor; get(index: number[] | number): number; set(index: number[] | number, value: number): void; setValues(values: Tensor, starts: number[]): Tensor; exp(): Tensor; log(): Tensor; sqrt(): Tensor; abs(): Tensor; sin(): Tensor; cos(): Tensor; tan(): Tensor; asin(): Tensor; acos(): Tensor; atan(): Tensor; sinh(): Tensor; cosh(): Tensor; tanh(): Tensor; asinh(): Tensor; acosh(): Tensor; atanh(): Tensor; floor(): Tensor; ceil(): Tensor; round(): Tensor; negate(): Tensor; powerScalar(power: number, factor: number): Tensor; multiplyScalar(value: number): Tensor; addScalar(value: number): Tensor; addMultiplyScalar(factor: number, add: number): Tensor; sign(): Tensor; clip(min?: number, max?: number): Tensor; clipBackward(grad: Tensor, min?: number, max?: number): Tensor; sigmoid(): Tensor; hardSigmoid(alpha: number, beta: number): Tensor; add_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[], alpha: number, beta: number): Tensor; subtract_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[], alpha: number, beta: number): Tensor; multiply_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[], alpha: number): Tensor; divide_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[], alpha: number): Tensor; power_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[]): Tensor; matMul(tensor: Tensor): Tensor; gemm_impl(b: Tensor, aTranspose: boolean, bTranspose: boolean, alpha: number, beta: number, c?: Tensor): Tensor; sum_impl(axes: number[], keepDims: boolean): Tensor; sumSquare_impl(axes: number[], keepDims: boolean): Tensor; product_impl(axes: number[], keepDims: boolean): Tensor; max_impl(axes: number[], keepDims: boolean): Tensor; min_impl(axes: number[], keepDims: boolean): Tensor; reduceMean_impl(axes: number[], keepDims: boolean): Tensor; reduceMeanSquare_impl(axes: number[], keepDims: boolean): Tensor; reduceLogSum_impl(axes: number[], keepDims: boolean): Tensor; reduceLogSumExp_impl(axes: number[], keepDims: boolean): Tensor; conv_impl(kernel: Tensor, dilations: number[], group: number, pads: number[], strides: number[], activation: Activation, bias?: Tensor): Tensor; protected convTranspose_impl(kernel: Tensor, dilations: number[], group: number, pads: number[], strides: number[]): Tensor; pad_impl(pads: number[], mode: PadMode, value: number): Tensor; averagePool_impl(kernelShape: number[], pads: number[], strides: number[], includePad: boolean): Tensor; reshape_impl(shape: number[], copy: boolean): Tensor; concat(tensor: Tensor, axis: number): Tensor; transpose_impl(permutation: number[]): Tensor; repeat(repeats: number[]): Tensor; expand(shape: readonly number[]): Tensor; gather(axis: number, indices: CPUTensor<'uint32'>): Tensor; slice_impl(starts: number[], ends: number[], axes: number[], steps: number[]): Tensor; upsample(scales: number[]): Tensor; normalize(mean: Tensor, variance: Tensor, epsilon: number, scale: Tensor, bias: Tensor): Tensor; }