/**
 * Transposes a tensor according to the provided axes.
 * @param {any} tensor The input tensor to transpose.
 * @param {Array} axes The axes to transpose the tensor along.
 * @returns {Tensor} The transposed tensor.
 */
export function transpose(tensor: any, axes: any[]): Tensor;
/**
 * Interpolates an Tensor to the given size.
 * @param {Tensor} input The input tensor to interpolate. Data must be channel-first (i.e., [c, h, w])
 * @param {number[]} size The output size of the image
 * @param {string} mode The interpolation mode
 * @param {boolean} align_corners Whether to align corners.
 * @returns {Tensor} The interpolated tensor.
 */
export function interpolate(input: Tensor, [out_height, out_width]: number[], mode?: string, align_corners?: boolean): Tensor;
/**
 * Perform mean pooling of the last hidden state followed by a normalization step.
 * @param {Tensor} last_hidden_state Tensor of shape [batchSize, seqLength, embedDim]
 * @param {Tensor} attention_mask Tensor of shape [batchSize, seqLength]
 * @returns {Tensor} Returns a new Tensor of shape [batchSize, embedDim].
 */
export function mean_pooling(last_hidden_state: Tensor, attention_mask: Tensor): Tensor;
/**
 * Concatenates an array of tensors along a specified dimension.
 * @param {Tensor[]} tensors The array of tensors to concatenate.
 * @param {number} dim The dimension to concatenate along.
 * @returns {Tensor} The concatenated tensor.
 */
export function cat(tensors: Tensor[], dim?: number): Tensor;
/**
 * Stack an array of tensors along a specified dimension.
 * @param {Tensor[]} tensors The array of tensors to stack.
 * @param {number} dim The dimension to stack along.
 * @returns {Tensor} The stacked tensor.
 */
export function stack(tensors: Tensor[], dim?: number): Tensor;
/**
 * Calculates the standard deviation and mean over the dimensions specified by dim. dim can be a single dimension or `null` to reduce over all dimensions.
 * @param {Tensor} input the input tenso
 * @param {number|null} dim the dimension to reduce. If None, all dimensions are reduced.
 * @param {number} correction difference between the sample size and sample degrees of freedom. Defaults to Bessel's correction, correction=1.
 * @param {boolean} keepdim whether the output tensor has dim retained or not.
 * @returns {Tensor[]} A tuple of (std, mean) tensors.
 */
export function std_mean(input: Tensor, dim?: number | null, correction?: number, keepdim?: boolean): Tensor[];
/**
 * Returns the mean value of each row of the input tensor in the given dimension dim.
 * @param {Tensor} input the input tensor.
 * @param {number|null} dim the dimension to reduce.
 * @param {boolean} keepdim whether the output tensor has dim retained or not.
 * @returns A new tensor with means taken along the specified dimension.
 */
export function mean(input: Tensor, dim?: number | null, keepdim?: boolean): Tensor;
/**
 *
 * Measures similarity between two temporal sequences (e.g., input audio and output tokens
 * to generate token-level timestamps).
 * @param {Tensor} matrix
 * @returns {number[][]}
 */
export function dynamicTimeWarping(matrix: Tensor): number[][];
/**
 * Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument size.
 * @param {number[]} size A sequence of integers defining the shape of the output tensor.
 */
export function ones(size: number[]): Tensor;
/**
 * Returns a tensor filled with the scalar value 1, with the same size as input.
 * @param {Tensor} tensor The size of input will determine size of the output tensor.
 * @returns The ones tensor.
 */
export function ones_like(tensor: Tensor): Tensor;
export class Tensor {
    /**
     * Create a new Tensor or copy an existing Tensor.
     * @param {[DataType, DataArray, number[]]|[import('onnxruntime-common').Tensor]} args
     */
    constructor(...args: [DataType, DataArray, number[]] | [import('onnxruntime-common').Tensor]);
    /** @type {number[]} Dimensions of the tensor. */
    dims: number[];
    /** @type {DataType} Type of the tensor. */
    type: DataType;
    /** @type {DataArray} The data stored in the tensor. */
    data: DataArray;
    /** @type {number} The number of elements in the tensor. */
    size: number;
    /**
     * Index into a Tensor object.
     * @param {number} index The index to access.
     * @returns {Tensor} The data at the specified index.
     */
    _getitem(index: number): Tensor;
    /**
     * @param {number|bigint} item The item to search for in the tensor
     * @returns {number} The index of the first occurrence of item in the tensor data.
     */
    indexOf(item: number | bigint): number;
    /**
     * @param {number} index
     * @param {number} iterSize
     * @param {any} iterDims
     * @returns {Tensor}
     */
    _subarray(index: number, iterSize: number, iterDims: any): Tensor;
    /**
     * Returns the value of this tensor as a standard JavaScript Number. This only works
     * for tensors with one element. For other cases, see `Tensor.tolist()`.
     * @returns {number|bigint} The value of this tensor as a standard JavaScript Number.
     * @throws {Error} If the tensor has more than one element.
     */
    item(): number | bigint;
    /**
     * Convert tensor data to a n-dimensional JS list
     * @returns {Array}
     */
    tolist(): any[];
    /**
     * Return a new Tensor with the sigmoid function applied to each element.
     * @returns {Tensor} The tensor with the sigmoid function applied.
     */
    sigmoid(): Tensor;
    /**
     * Applies the sigmoid function to the tensor in place.
     * @returns {Tensor} Returns `this`.
     */
    sigmoid_(): Tensor;
    /**
     * Return a new Tensor with every element multiplied by a constant.
     * @param {number} val The value to multiply by.
     * @returns {Tensor} The new tensor.
     */
    mul(val: number): Tensor;
    /**
     * Multiply the tensor by a constant in place.
     * @param {number} val The value to multiply by.
     * @returns {Tensor} Returns `this`.
     */
    mul_(val: number): Tensor;
    /**
     * Return a new Tensor with every element added by a constant.
     * @param {number} val The value to add by.
     * @returns {Tensor} The new tensor.
     */
    add(val: number): Tensor;
    /**
     * Add the tensor by a constant in place.
     * @param {number} val The value to add by.
     * @returns {Tensor} Returns `this`.
     */
    add_(val: number): Tensor;
    clone(): Tensor;
    slice(...slices: any[]): Tensor;
    /**
     * Return a transposed version of this Tensor, according to the provided dimensions.
     * @param  {...number} dims Dimensions to transpose.
     * @returns {Tensor} The transposed tensor.
     */
    transpose(...dims: number[]): Tensor;
    /**
     * Returns the sum of each row of the input tensor in the given dimension dim.
     *
     * @param {number} [dim=null] The dimension or dimensions to reduce. If `null`, all dimensions are reduced.
     * @param {boolean} keepdim Whether the output tensor has `dim` retained or not.
     * @returns The summed tensor
     */
    sum(dim?: number | undefined, keepdim?: boolean): Tensor;
    /**
     * Returns the matrix norm or vector norm of a given tensor.
     * @param {number|string} [p='fro'] The order of norm
     * @param {number} [dim=null] Specifies which dimension of the tensor to calculate the norm across.
     * If dim is None, the norm will be calculated across all dimensions of input.
     * @param {boolean} [keepdim=false] Whether the output tensors have dim retained or not.
     * @returns {Tensor} The norm of the tensor.
     */
    norm(p?: string | number | undefined, dim?: number | undefined, keepdim?: boolean | undefined): Tensor;
    /**
     * Performs `L_p` normalization of inputs over specified dimension. Operates in place.
     * @param {number} [p=2] The exponent value in the norm formulation
     * @param {number} [dim=1] The dimension to reduce
     * @returns {Tensor} `this` for operation chaining.
     */
    normalize_(p?: number | undefined, dim?: number | undefined): Tensor;
    /**
     * Performs `L_p` normalization of inputs over specified dimension.
     * @param {number} [p=2] The exponent value in the norm formulation
     * @param {number} [dim=1] The dimension to reduce
     * @returns {Tensor} The normalized tensor.
     */
    normalize(p?: number | undefined, dim?: number | undefined): Tensor;
    /**
     * Compute and return the stride of this tensor.
     * Stride is the jump necessary to go from one element to the next one in the specified dimension dim.
     * @returns {number[]} The stride of this tensor.
     */
    stride(): number[];
    /**
     * Returns a tensor with all specified dimensions of input of size 1 removed.
     *
     * NOTE: The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.
     * If you would like a copy, use `tensor.clone()` before squeezing.
     *
     * @param {number} [dim=null] If given, the input will be squeezed only in the specified dimensions.
     * @returns The squeezed tensor
     */
    squeeze(dim?: number | undefined): Tensor;
    /**
     * In-place version of @see {@link Tensor.squeeze}
     */
    squeeze_(dim?: null): this;
    /**
     * Returns a new tensor with a dimension of size one inserted at the specified position.
     *
     * NOTE: The returned tensor shares the same underlying data with this tensor.
     *
     * @param {number} dim The index at which to insert the singleton dimension
     * @returns The unsqueezed tensor
     */
    unsqueeze(dim?: number): Tensor;
    /**
     * In-place version of @see {@link Tensor.unsqueeze}
     */
    unsqueeze_(dim?: null): this;
    /**
     * In-place version of @see {@link Tensor.flatten}
     */
    flatten_(start_dim?: number, end_dim?: number): this;
    /**
     * Flattens input by reshaping it into a one-dimensional tensor.
     * If `start_dim` or `end_dim` are passed, only dimensions starting with `start_dim`
     * and ending with `end_dim` are flattened. The order of elements in input is unchanged.
     * @param {number} start_dim the first dim to flatten
     * @param {number} end_dim the last dim to flatten
     * @returns The flattened tensor.
     */
    flatten(start_dim?: number, end_dim?: number): Tensor;
    /**
     * Returns a new tensor with the same data as the `self` tensor but of a different `shape`.
     * @param  {...number} dims the desired size
     * @returns {Tensor} The tensor with the same data but different shape
     */
    view(...dims: number[]): Tensor;
    neg_(): this;
    neg(): Tensor;
    /**
     * In-place version of @see {@link Tensor.clamp}
     */
    clamp_(min: any, max: any): this;
    /**
     * Clamps all elements in input into the range [ min, max ]
     * @param {number} min lower-bound of the range to be clamped to
     * @param {number} max upper-bound of the range to be clamped to
     * @returns the output tensor.
     */
    clamp(min: number, max: number): Tensor;
    /**
     * In-place version of @see {@link Tensor.round}
     */
    round_(): this;
    /**
     * Rounds elements of input to the nearest integer.
     * @returns the output tensor.
     */
    round(): Tensor;
    /**
     * Performs Tensor dtype conversion.
     * @param {DataType} type The desired data type.
     * @returns {Tensor} The converted tensor.
     */
    to(type: DataType): Tensor;
    /**
     * Returns an iterator object for iterating over the tensor data in row-major order.
     * If the tensor has more than one dimension, the iterator will yield subarrays.
     * @returns {Iterator} An iterator object for iterating over the tensor data in row-major order.
     */
    [Symbol.iterator](): Iterator<any, any, undefined>;
}
/**
 * This creates a nested array of a given type and depth (see examples).
 */
export type NestArray<T, Depth extends number, Acc extends never[] = []> = Acc['length'] extends Depth ? T : NestArray<T[], Depth, [...Acc, never]>;
export type DataType = keyof typeof DataTypeMap;
export type DataArray = import('./maths.js').AnyTypedArray | any[];
declare const DataTypeMap: Readonly<{
    float32: Float32ArrayConstructor;
    float64: Float64ArrayConstructor;
    string: ArrayConstructor;
    int8: Int8ArrayConstructor;
    uint8: Uint8ArrayConstructor;
    int16: Int16ArrayConstructor;
    uint16: Uint16ArrayConstructor;
    int32: Int32ArrayConstructor;
    uint32: Uint32ArrayConstructor;
    int64: BigInt64ArrayConstructor;
    uint64: BigUint64ArrayConstructor;
    bool: Uint8ArrayConstructor;
}>;
export {};
//# sourceMappingURL=tensor.d.ts.map