Buckets:
| # utils/tensor | |
| Helper module for `Tensor` processing. | |
| These functions and classes are only used internally, | |
| meaning an end-user shouldn't need to access anything here. | |
| * [utils/tensor](#module_utils/tensor) | |
| * _static_ | |
| * [.Tensor](#module_utils/tensor.Tensor) | |
| * [`new Tensor(...args)`](#new_module_utils/tensor.Tensor_new) | |
| * [`.dims`](#module_utils/tensor.Tensor+dims) : Array | |
| * [`.type`](#module_utils/tensor.Tensor+type) : [DataType](#DataType) | |
| * [`.data`](#module_utils/tensor.Tensor+data) : DataArray | |
| * [`.size`](#module_utils/tensor.Tensor+size) : number | |
| * [`.location`](#module_utils/tensor.Tensor+location) : string | |
| * [`.Symbol.iterator()`](#module_utils/tensor.Tensor+Symbol.iterator) ⇒ Iterator.<any> | |
| * [`._getitem(index)`](#module_utils/tensor.Tensor+_getitem) ⇒ [Tensor](#Tensor) | |
| * [`.indexOf(item)`](#module_utils/tensor.Tensor+indexOf) ⇒ number | |
| * [`._subarray(index, iterSize, iterDims)`](#module_utils/tensor.Tensor+_subarray) ⇒ [Tensor](#Tensor) | |
| * [`.item()`](#module_utils/tensor.Tensor+item) ⇒ number | bigint | |
| * [`.tolist()`](#module_utils/tensor.Tensor+tolist) ⇒ Array | |
| * [`.sigmoid()`](#module_utils/tensor.Tensor+sigmoid) ⇒ [Tensor](#Tensor) | |
| * [`.sigmoid_()`](#module_utils/tensor.Tensor+sigmoid_) ⇒ [Tensor](#Tensor) | |
| * [`.map(callback)`](#module_utils/tensor.Tensor+map) ⇒ [Tensor](#Tensor) | |
| * [`.map_(callback)`](#module_utils/tensor.Tensor+map_) ⇒ [Tensor](#Tensor) | |
| * [`.mul(val)`](#module_utils/tensor.Tensor+mul) ⇒ [Tensor](#Tensor) | |
| * [`.mul_(val)`](#module_utils/tensor.Tensor+mul_) ⇒ [Tensor](#Tensor) | |
| * [`.div(val)`](#module_utils/tensor.Tensor+div) ⇒ [Tensor](#Tensor) | |
| * [`.div_(val)`](#module_utils/tensor.Tensor+div_) ⇒ [Tensor](#Tensor) | |
| * [`.add(val)`](#module_utils/tensor.Tensor+add) ⇒ [Tensor](#Tensor) | |
| * [`.add_(val)`](#module_utils/tensor.Tensor+add_) ⇒ [Tensor](#Tensor) | |
| * [`.sub(val)`](#module_utils/tensor.Tensor+sub) ⇒ [Tensor](#Tensor) | |
| * [`.sub_(val)`](#module_utils/tensor.Tensor+sub_) ⇒ [Tensor](#Tensor) | |
| * [`.clone()`](#module_utils/tensor.Tensor+clone) ⇒ [Tensor](#Tensor) | |
| * [`.slice(...slices)`](#module_utils/tensor.Tensor+slice) ⇒ [Tensor](#Tensor) | |
| * [`.permute(...dims)`](#module_utils/tensor.Tensor+permute) ⇒ [Tensor](#Tensor) | |
| * [`.transpose()`](#module_utils/tensor.Tensor+transpose) : [Tensor](#Tensor) | |
| * [`.sum([dim], keepdim)`](#module_utils/tensor.Tensor+sum) ⇒ | |
| * [`.norm([p], [dim], [keepdim])`](#module_utils/tensor.Tensor+norm) ⇒ [Tensor](#Tensor) | |
| * [`.normalize_([p], [dim])`](#module_utils/tensor.Tensor+normalize_) ⇒ [Tensor](#Tensor) | |
| * [`.normalize([p], [dim])`](#module_utils/tensor.Tensor+normalize) ⇒ [Tensor](#Tensor) | |
| * [`.stride()`](#module_utils/tensor.Tensor+stride) ⇒ Array | |
| * [`.squeeze([dim])`](#module_utils/tensor.Tensor+squeeze) ⇒ [Tensor](#Tensor) | |
| * [`.squeeze_()`](#module_utils/tensor.Tensor+squeeze_) | |
| * [`.unsqueeze(dim)`](#module_utils/tensor.Tensor+unsqueeze) ⇒ [Tensor](#Tensor) | |
| * [`.unsqueeze_()`](#module_utils/tensor.Tensor+unsqueeze_) : [Tensor](#Tensor) | |
| * [`.flatten_()`](#module_utils/tensor.Tensor+flatten_) | |
| * [`.flatten(start_dim, end_dim)`](#module_utils/tensor.Tensor+flatten) ⇒ [Tensor](#Tensor) | |
| * [`.view(...dims)`](#module_utils/tensor.Tensor+view) ⇒ [Tensor](#Tensor) | |
| * [`.gt(val)`](#module_utils/tensor.Tensor+gt) ⇒ [Tensor](#Tensor) | |
| * [`.lt(val)`](#module_utils/tensor.Tensor+lt) ⇒ [Tensor](#Tensor) | |
| * [`.clamp_()`](#module_utils/tensor.Tensor+clamp_) : [Tensor](#Tensor) | |
| * [`.clamp(min, max)`](#module_utils/tensor.Tensor+clamp) ⇒ [Tensor](#Tensor) | |
| * [`.round_()`](#module_utils/tensor.Tensor+round_) | |
| * [`.round()`](#module_utils/tensor.Tensor+round) ⇒ [Tensor](#Tensor) | |
| * [`.repeat(...repeats)`](#module_utils/tensor.Tensor+repeat) ⇒ [Tensor](#Tensor) | |
| * [`.tile(...dims)`](#module_utils/tensor.Tensor+tile) ⇒ [Tensor](#Tensor) | |
| * [`.to(type)`](#module_utils/tensor.Tensor+to) ⇒ [Tensor](#Tensor) | |
| * [`.permute(tensor, axes)`](#module_utils/tensor.permute) ⇒ [Tensor](#Tensor) | |
| * [`.interpolate(input, size, mode, align_corners)`](#module_utils/tensor.interpolate) ⇒ [Tensor](#Tensor) | |
| * [`.interpolate_4d(input, options)`](#module_utils/tensor.interpolate_4d) ⇒ [Promise.<Tensor>](#Tensor) | |
| * [`.matmul(a, b)`](#module_utils/tensor.matmul) ⇒ [Promise.<Tensor>](#Tensor) | |
| * [`.rfft(x, a)`](#module_utils/tensor.rfft) ⇒ [Promise.<Tensor>](#Tensor) | |
| * [`.topk(x, [k])`](#module_utils/tensor.topk) ⇒ Promise.<Array> | |
| * [`.slice(data:, starts:, ends:, axes:, [steps])`](#module_utils/tensor.slice) ⇒ [Promise.<Tensor>](#Tensor) | |
| * [`.mean_pooling(last_hidden_state, attention_mask)`](#module_utils/tensor.mean_pooling) ⇒ [Tensor](#Tensor) | |
| * [`.layer_norm(input, normalized_shape, options)`](#module_utils/tensor.layer_norm) ⇒ [Tensor](#Tensor) | |
| * [`.cat(tensors, dim)`](#module_utils/tensor.cat) ⇒ [Tensor](#Tensor) | |
| * [`.stack(tensors, dim)`](#module_utils/tensor.stack) ⇒ [Tensor](#Tensor) | |
| * [`.std_mean(input, dim, correction, keepdim)`](#module_utils/tensor.std_mean) ⇒ Array | |
| * [`.mean(input, dim, keepdim)`](#module_utils/tensor.mean) ⇒ [Tensor](#Tensor) | |
| * [`.full(size, fill_value)`](#module_utils/tensor.full) ⇒ [Tensor](#Tensor) | |
| * [`.ones(size)`](#module_utils/tensor.ones) ⇒ [Tensor](#Tensor) | |
| * [`.ones_like(tensor)`](#module_utils/tensor.ones_like) ⇒ [Tensor](#Tensor) | |
| * [`.zeros(size)`](#module_utils/tensor.zeros) ⇒ [Tensor](#Tensor) | |
| * [`.zeros_like(tensor)`](#module_utils/tensor.zeros_like) ⇒ [Tensor](#Tensor) | |
| * [`.rand(size)`](#module_utils/tensor.rand) ⇒ [Tensor](#Tensor) | |
| * [`.randn(size)`](#module_utils/tensor.randn) ⇒ [Tensor](#Tensor) | |
| * [`.quantize_embeddings(tensor, precision)`](#module_utils/tensor.quantize_embeddings) ⇒ [Tensor](#Tensor) | |
| * _inner_ | |
| * [`~args[0]`](#module_utils/tensor..args[0]) : ONNXTensor | |
| * [`~reshape(data, dimensions)`](#module_utils/tensor..reshape) ⇒ NestArray.<T, DIM> | |
| * [`~reshapedArray`](#module_utils/tensor..reshape..reshapedArray) : any | |
| * [`~reduce_helper(callbackfn, input, dim, keepdim, [initialValue])`](#module_utils/tensor..reduce_helper) ⇒ Array | |
| * [`~DataArray`](#module_utils/tensor..DataArray) : string | |
| * [`~NestArray`](#module_utils/tensor..NestArray) : any | |
| * * * | |
| ## utils/tensor.Tensor | |
| **Kind**: static class of [utils/tensor](#module_utils/tensor) | |
| * [.Tensor](#module_utils/tensor.Tensor) | |
| * [`new Tensor(...args)`](#new_module_utils/tensor.Tensor_new) | |
| * [`.dims`](#module_utils/tensor.Tensor+dims) : Array | |
| * [`.type`](#module_utils/tensor.Tensor+type) : [DataType](#DataType) | |
| * [`.data`](#module_utils/tensor.Tensor+data) : DataArray | |
| * [`.size`](#module_utils/tensor.Tensor+size) : number | |
| * [`.location`](#module_utils/tensor.Tensor+location) : string | |
| * [`.Symbol.iterator()`](#module_utils/tensor.Tensor+Symbol.iterator) ⇒ Iterator.<any> | |
| * [`._getitem(index)`](#module_utils/tensor.Tensor+_getitem) ⇒ [Tensor](#Tensor) | |
| * [`.indexOf(item)`](#module_utils/tensor.Tensor+indexOf) ⇒ number | |
| * [`._subarray(index, iterSize, iterDims)`](#module_utils/tensor.Tensor+_subarray) ⇒ [Tensor](#Tensor) | |
| * [`.item()`](#module_utils/tensor.Tensor+item) ⇒ number | bigint | |
| * [`.tolist()`](#module_utils/tensor.Tensor+tolist) ⇒ Array | |
| * [`.sigmoid()`](#module_utils/tensor.Tensor+sigmoid) ⇒ [Tensor](#Tensor) | |
| * [`.sigmoid_()`](#module_utils/tensor.Tensor+sigmoid_) ⇒ [Tensor](#Tensor) | |
| * [`.map(callback)`](#module_utils/tensor.Tensor+map) ⇒ [Tensor](#Tensor) | |
| * [`.map_(callback)`](#module_utils/tensor.Tensor+map_) ⇒ [Tensor](#Tensor) | |
| * [`.mul(val)`](#module_utils/tensor.Tensor+mul) ⇒ [Tensor](#Tensor) | |
| * [`.mul_(val)`](#module_utils/tensor.Tensor+mul_) ⇒ [Tensor](#Tensor) | |
| * [`.div(val)`](#module_utils/tensor.Tensor+div) ⇒ [Tensor](#Tensor) | |
| * [`.div_(val)`](#module_utils/tensor.Tensor+div_) ⇒ [Tensor](#Tensor) | |
| * [`.add(val)`](#module_utils/tensor.Tensor+add) ⇒ [Tensor](#Tensor) | |
| * [`.add_(val)`](#module_utils/tensor.Tensor+add_) ⇒ [Tensor](#Tensor) | |
| * [`.sub(val)`](#module_utils/tensor.Tensor+sub) ⇒ [Tensor](#Tensor) | |
| * [`.sub_(val)`](#module_utils/tensor.Tensor+sub_) ⇒ [Tensor](#Tensor) | |
| * [`.clone()`](#module_utils/tensor.Tensor+clone) ⇒ [Tensor](#Tensor) | |
| * [`.slice(...slices)`](#module_utils/tensor.Tensor+slice) ⇒ [Tensor](#Tensor) | |
| * [`.permute(...dims)`](#module_utils/tensor.Tensor+permute) ⇒ [Tensor](#Tensor) | |
| * [`.transpose()`](#module_utils/tensor.Tensor+transpose) : [Tensor](#Tensor) | |
| * [`.sum([dim], keepdim)`](#module_utils/tensor.Tensor+sum) ⇒ | |
| * [`.norm([p], [dim], [keepdim])`](#module_utils/tensor.Tensor+norm) ⇒ [Tensor](#Tensor) | |
| * [`.normalize_([p], [dim])`](#module_utils/tensor.Tensor+normalize_) ⇒ [Tensor](#Tensor) | |
| * [`.normalize([p], [dim])`](#module_utils/tensor.Tensor+normalize) ⇒ [Tensor](#Tensor) | |
| * [`.stride()`](#module_utils/tensor.Tensor+stride) ⇒ Array | |
| * [`.squeeze([dim])`](#module_utils/tensor.Tensor+squeeze) ⇒ [Tensor](#Tensor) | |
| * [`.squeeze_()`](#module_utils/tensor.Tensor+squeeze_) | |
| * [`.unsqueeze(dim)`](#module_utils/tensor.Tensor+unsqueeze) ⇒ [Tensor](#Tensor) | |
| * [`.unsqueeze_()`](#module_utils/tensor.Tensor+unsqueeze_) : [Tensor](#Tensor) | |
| * [`.flatten_()`](#module_utils/tensor.Tensor+flatten_) | |
| * [`.flatten(start_dim, end_dim)`](#module_utils/tensor.Tensor+flatten) ⇒ [Tensor](#Tensor) | |
| * [`.view(...dims)`](#module_utils/tensor.Tensor+view) ⇒ [Tensor](#Tensor) | |
| * [`.gt(val)`](#module_utils/tensor.Tensor+gt) ⇒ [Tensor](#Tensor) | |
| * [`.lt(val)`](#module_utils/tensor.Tensor+lt) ⇒ [Tensor](#Tensor) | |
| * [`.clamp_()`](#module_utils/tensor.Tensor+clamp_) : [Tensor](#Tensor) | |
| * [`.clamp(min, max)`](#module_utils/tensor.Tensor+clamp) ⇒ [Tensor](#Tensor) | |
| * [`.round_()`](#module_utils/tensor.Tensor+round_) | |
| * [`.round()`](#module_utils/tensor.Tensor+round) ⇒ [Tensor](#Tensor) | |
| * [`.repeat(...repeats)`](#module_utils/tensor.Tensor+repeat) ⇒ [Tensor](#Tensor) | |
| * [`.tile(...dims)`](#module_utils/tensor.Tensor+tile) ⇒ [Tensor](#Tensor) | |
| * [`.to(type)`](#module_utils/tensor.Tensor+to) ⇒ [Tensor](#Tensor) | |
| * * * | |
| ### `new Tensor(...args)` | |
| Create a new Tensor or copy an existing Tensor. | |
| ParamType | |
| ...argsArray | Array | |
| * * * | |
| ### `tensor.dims` : Array | |
| Dimensions of the tensor. | |
| **Kind**: instance property of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.type` : [DataType](#DataType) | |
| Type of the tensor. | |
| **Kind**: instance property of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.data` : DataArray | |
| The data stored in the tensor. | |
| **Kind**: instance property of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.size` : number | |
| The number of elements in the tensor. | |
| **Kind**: instance property of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.location` : string | |
| The location of the tensor data. | |
| **Kind**: instance property of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.Symbol.iterator()` ⇒ Iterator.<any> | |
| 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. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: Iterator.<any> - An iterator object for iterating over the tensor data in row-major order. | |
| * * * | |
| ### `tensor._getitem(index)` ⇒ [Tensor](#Tensor) | |
| Index into a Tensor object. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The data at the specified index. | |
| ParamTypeDescription | |
| indexnumberThe index to access. | |
| * * * | |
| ### `tensor.indexOf(item)` ⇒ number | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: number - The index of the first occurrence of item in the tensor data. | |
| ParamTypeDescription | |
| itemnumber | bigintThe item to search for in the tensor | |
| * * * | |
| ### `tensor._subarray(index, iterSize, iterDims)` ⇒ [Tensor](#Tensor) | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| ParamType | |
| indexnumber | |
| iterSizenumber | |
| iterDimsany | |
| * * * | |
| ### `tensor.item()` ⇒ number | bigint | |
| 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()`. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: number | bigint - The value of this tensor as a standard JavaScript Number. | |
| **Throws**: | |
| - Error If the tensor has more than one element. | |
| * * * | |
| ### `tensor.tolist()` ⇒ Array | |
| Convert tensor data to a n-dimensional JS list | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.sigmoid()` ⇒ [Tensor](#Tensor) | |
| Return a new Tensor with the sigmoid function applied to each element. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The tensor with the sigmoid function applied. | |
| * * * | |
| ### `tensor.sigmoid_()` ⇒ [Tensor](#Tensor) | |
| Applies the sigmoid function to the tensor in place. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - Returns `this`. | |
| * * * | |
| ### `tensor.map(callback)` ⇒ [Tensor](#Tensor) | |
| Return a new Tensor with a callback function applied to each element. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - A new Tensor with the callback function applied to each element. | |
| ParamTypeDescription | |
| callbackfunctionThe function to apply to each element. It should take three arguments: | |
| the current element, its index, and the tensor's data array. | |
| * * * | |
| ### `tensor.map_(callback)` ⇒ [Tensor](#Tensor) | |
| Apply a callback function to each element of the tensor in place. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - Returns `this`. | |
| ParamTypeDescription | |
| callbackfunctionThe function to apply to each element. It should take three arguments: | |
| the current element, its index, and the tensor's data array. | |
| * * * | |
| ### `tensor.mul(val)` ⇒ [Tensor](#Tensor) | |
| Return a new Tensor with every element multiplied by a constant. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The new tensor. | |
| ParamTypeDescription | |
| valnumberThe value to multiply by. | |
| * * * | |
| ### `tensor.mul_(val)` ⇒ [Tensor](#Tensor) | |
| Multiply the tensor by a constant in place. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - Returns `this`. | |
| ParamTypeDescription | |
| valnumberThe value to multiply by. | |
| * * * | |
| ### `tensor.div(val)` ⇒ [Tensor](#Tensor) | |
| Return a new Tensor with every element divided by a constant. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The new tensor. | |
| ParamTypeDescription | |
| valnumberThe value to divide by. | |
| * * * | |
| ### `tensor.div_(val)` ⇒ [Tensor](#Tensor) | |
| Divide the tensor by a constant in place. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - Returns `this`. | |
| ParamTypeDescription | |
| valnumberThe value to divide by. | |
| * * * | |
| ### `tensor.add(val)` ⇒ [Tensor](#Tensor) | |
| Return a new Tensor with every element added by a constant. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The new tensor. | |
| ParamTypeDescription | |
| valnumberThe value to add by. | |
| * * * | |
| ### `tensor.add_(val)` ⇒ [Tensor](#Tensor) | |
| Add the tensor by a constant in place. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - Returns `this`. | |
| ParamTypeDescription | |
| valnumberThe value to add by. | |
| * * * | |
| ### `tensor.sub(val)` ⇒ [Tensor](#Tensor) | |
| Return a new Tensor with every element subtracted by a constant. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The new tensor. | |
| ParamTypeDescription | |
| valnumberThe value to subtract by. | |
| * * * | |
| ### `tensor.sub_(val)` ⇒ [Tensor](#Tensor) | |
| Subtract the tensor by a constant in place. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - Returns `this`. | |
| ParamTypeDescription | |
| valnumberThe value to subtract by. | |
| * * * | |
| ### `tensor.clone()` ⇒ [Tensor](#Tensor) | |
| Creates a deep copy of the current Tensor. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - A new Tensor with the same type, data, and dimensions as the original. | |
| * * * | |
| ### `tensor.slice(...slices)` ⇒ [Tensor](#Tensor) | |
| Performs a slice operation on the Tensor along specified dimensions. | |
| Consider a Tensor that has a dimension of [4, 7]: | |
| ``` | |
| [ 1, 2, 3, 4, 5, 6, 7] | |
| [ 8, 9, 10, 11, 12, 13, 14] | |
| [15, 16, 17, 18, 19, 20, 21] | |
| [22, 23, 24, 25, 26, 27, 28] | |
| ``` | |
| We can slice against the two dims of row and column, for instance in this | |
| case we can start at the second element, and return to the second last, | |
| like this: | |
| ``` | |
| tensor.slice([1, -1], [1, -1]); | |
| ``` | |
| which would return: | |
| ``` | |
| [ 9, 10, 11, 12, 13 ] | |
| [ 16, 17, 18, 19, 20 ] | |
| ``` | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - A new Tensor containing the selected elements. | |
| **Throws**: | |
| - Error If the slice input is invalid. | |
| ParamTypeDescription | |
| ...slicesnumber | Array | nullThe slice specifications for each dimension. | |
| If a number is given, then a single element is selected. | |
| If an array of two numbers is given, then a range of elements [start, end (exclusive)] is selected. | |
| If null is given, then the entire dimension is selected. | |
| * * * | |
| ### `tensor.permute(...dims)` ⇒ [Tensor](#Tensor) | |
| Return a permuted version of this Tensor, according to the provided dimensions. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The permuted tensor. | |
| ParamTypeDescription | |
| ...dimsnumberDimensions to permute. | |
| * * * | |
| ### `tensor.transpose()` : [Tensor](#Tensor) | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.sum([dim], keepdim)` ⇒ | |
| Returns the sum of each row of the input tensor in the given dimension dim. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: The summed tensor | |
| ParamTypeDefaultDescription | |
| [dim]number | nullThe dimension or dimensions to reduce. If null, all dimensions are reduced. | |
| keepdimbooleanfalseWhether the output tensor has dim retained or not. | |
| * * * | |
| ### `tensor.norm([p], [dim], [keepdim])` ⇒ [Tensor](#Tensor) | |
| Returns the matrix norm or vector norm of a given tensor. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The norm of the tensor. | |
| ParamTypeDefaultDescription | |
| [p]number | string'fro'The order of norm | |
| [dim]number | nullSpecifies which dimension of the tensor to calculate the norm across. | |
| If dim is None, the norm will be calculated across all dimensions of input. | |
| [keepdim]booleanfalseWhether the output tensors have dim retained or not. | |
| * * * | |
| ### `tensor.normalize_([p], [dim])` ⇒ [Tensor](#Tensor) | |
| Performs `L_p` normalization of inputs over specified dimension. Operates in place. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - `this` for operation chaining. | |
| ParamTypeDefaultDescription | |
| [p]number2The exponent value in the norm formulation | |
| [dim]number1The dimension to reduce | |
| * * * | |
| ### `tensor.normalize([p], [dim])` ⇒ [Tensor](#Tensor) | |
| Performs `L_p` normalization of inputs over specified dimension. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The normalized tensor. | |
| ParamTypeDefaultDescription | |
| [p]number2The exponent value in the norm formulation | |
| [dim]number1The dimension to reduce | |
| * * * | |
| ### `tensor.stride()` ⇒ Array | |
| 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. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: Array - The stride of this tensor. | |
| * * * | |
| ### `tensor.squeeze([dim])` ⇒ [Tensor](#Tensor) | |
| 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. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The squeezed tensor | |
| ParamTypeDefaultDescription | |
| [dim]number | Array | nullIf given, the input will be squeezed only in the specified dimensions. | |
| * * * | |
| ### `tensor.squeeze_()` | |
| In-place version of @see [Tensor.squeeze](Tensor.squeeze) | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.unsqueeze(dim)` ⇒ [Tensor](#Tensor) | |
| 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. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The unsqueezed tensor | |
| ParamTypeDescription | |
| dimnumberThe index at which to insert the singleton dimension | |
| * * * | |
| ### `tensor.unsqueeze_()` : [Tensor](#Tensor) | |
| In-place version of @see [Tensor.unsqueeze](Tensor.unsqueeze) | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.flatten_()` | |
| In-place version of @see [Tensor.flatten](Tensor.flatten) | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.flatten(start_dim, end_dim)` ⇒ [Tensor](#Tensor) | |
| 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. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The flattened tensor. | |
| ParamTypeDefaultDescription | |
| start_dimnumber0the first dim to flatten | |
| end_dimnumberthe last dim to flatten | |
| * * * | |
| ### `tensor.view(...dims)` ⇒ [Tensor](#Tensor) | |
| Returns a new tensor with the same data as the `self` tensor but of a different `shape`. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The tensor with the same data but different shape | |
| ParamTypeDescription | |
| ...dimsnumberthe desired size | |
| * * * | |
| ### `tensor.gt(val)` ⇒ [Tensor](#Tensor) | |
| Computes input > val element-wise. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - A boolean tensor that is `true` where input is greater than other and `false` elsewhere. | |
| ParamTypeDescription | |
| valnumberThe value to compare with. | |
| * * * | |
| ### `tensor.lt(val)` ⇒ [Tensor](#Tensor) | |
| Computes input Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - A boolean tensor that is `true` where input is less than other and `false` elsewhere. | |
| ParamTypeDescription | |
| valnumberThe value to compare with. | |
| * * * | |
| ### `tensor.clamp_()` : [Tensor](#Tensor) | |
| In-place version of @see [Tensor.clamp](Tensor.clamp) | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.clamp(min, max)` ⇒ [Tensor](#Tensor) | |
| Clamps all elements in input into the range [ min, max ] | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - the output tensor. | |
| ParamTypeDescription | |
| minnumberlower-bound of the range to be clamped to | |
| maxnumberupper-bound of the range to be clamped to | |
| * * * | |
| ### `tensor.round_()` | |
| In-place version of @see [Tensor.round](Tensor.round) | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| * * * | |
| ### `tensor.round()` ⇒ [Tensor](#Tensor) | |
| Rounds elements of input to the nearest integer. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - the output tensor. | |
| * * * | |
| ### `tensor.repeat(...repeats)` ⇒ [Tensor](#Tensor) | |
| Repeats this tensor along the specified dimensions. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The repeated tensor. | |
| **Throws**: | |
| - Error If the number of repeats is less than the number of dimensions. | |
| ParamTypeDescription | |
| ...repeatsnumberThe number of times to repeat this tensor along each dimension. | |
| * * * | |
| ### `tensor.tile(...dims)` ⇒ [Tensor](#Tensor) | |
| Constructs a tensor by repeating the elements of input. The dims argument specifies the number of repetitions in each dimension. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The tiled tensor. | |
| ParamTypeDescription | |
| ...dimsnumberThe number of repetitions per dimension. | |
| * * * | |
| ### `tensor.to(type)` ⇒ [Tensor](#Tensor) | |
| Performs Tensor dtype conversion. | |
| **Kind**: instance method of [Tensor](#module_utils/tensor.Tensor) | |
| **Returns**: [Tensor](#Tensor) - The converted tensor. | |
| ParamTypeDescription | |
| typeDataTypeThe desired data type. | |
| * * * | |
| ## `utils/tensor.permute(tensor, axes)` ⇒ [Tensor](#Tensor) | |
| Permutes a tensor according to the provided axes. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The permuted tensor. | |
| ParamTypeDescription | |
| tensoranyThe input tensor to permute. | |
| axesArrayThe axes to permute the tensor along. | |
| * * * | |
| ## `utils/tensor.interpolate(input, size, mode, align_corners)` ⇒ [Tensor](#Tensor) | |
| Interpolates an Tensor to the given size. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The interpolated tensor. | |
| ParamTypeDescription | |
| inputTensorThe input tensor to interpolate. Data must be channel-first (i.e., [c, h, w]) | |
| sizeArrayThe output size of the image | |
| modestringThe interpolation mode | |
| align_cornersbooleanWhether to align corners. | |
| * * * | |
| ## `utils/tensor.interpolate_4d(input, options)` ⇒ [Promise.<Tensor>](#Tensor) | |
| Down/up samples the input. | |
| Inspired by https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Promise.<Tensor>](#Tensor) - The interpolated tensor. | |
| ParamTypeDefaultDescription | |
| inputTensorthe input tensor | |
| optionsObjectthe options for the interpolation | |
| [options.size]Array | Array | Arrayoutput spatial size. | |
| [options.mode]"nearest" | "bilinear" | "bicubic"'bilinear'algorithm used for upsampling | |
| * * * | |
| ## `utils/tensor.matmul(a, b)` ⇒ [Promise.<Tensor>](#Tensor) | |
| Matrix product of two tensors. | |
| Inspired by https://pytorch.org/docs/stable/generated/torch.matmul.html | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Promise.<Tensor>](#Tensor) - The matrix product of the two tensors. | |
| ParamTypeDescription | |
| aTensorthe first tensor to be multiplied | |
| bTensorthe second tensor to be multiplied | |
| * * * | |
| ## `utils/tensor.rfft(x, a)` ⇒ [Promise.<Tensor>](#Tensor) | |
| Computes the one dimensional Fourier transform of real-valued input. | |
| Inspired by https://pytorch.org/docs/stable/generated/torch.fft.rfft.html | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Promise.<Tensor>](#Tensor) - the output tensor. | |
| ParamTypeDescription | |
| xTensorthe real input tensor | |
| aTensorThe dimension along which to take the one dimensional real FFT. | |
| * * * | |
| ## `utils/tensor.topk(x, [k])` ⇒ Promise.<Array> | |
| Returns the k largest elements of the given input tensor. | |
| Inspired by https://pytorch.org/docs/stable/generated/torch.topk.html | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: Promise.<Array> - the output tuple of (Tensor, LongTensor) of top-k elements and their indices. | |
| ParamTypeDescription | |
| xTensorthe input tensor | |
| [k]numberthe k in "top-k" | |
| * * * | |
| ## `utils/tensor.slice(data:, starts:, ends:, axes:, [steps])` ⇒ [Promise.<Tensor>](#Tensor) | |
| Slice a multidimensional float32 tensor. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Promise.<Tensor>](#Tensor) - Sliced data tensor. | |
| ParamTypeDescription | |
| data:TensorTensor of data to extract slices from | |
| starts:Array1-D array of starting indices of corresponding axis in axes | |
| ends:Array1-D array of ending indices (exclusive) of corresponding axis in axes | |
| axes:Array1-D array of axes that starts and ends apply to | |
| [steps]Array1-D array of slice step of corresponding axis in axes. | |
| * * * | |
| ## `utils/tensor.mean_pooling(last_hidden_state, attention_mask)` ⇒ [Tensor](#Tensor) | |
| Perform mean pooling of the last hidden state followed by a normalization step. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - Returns a new Tensor of shape [batchSize, embedDim]. | |
| ParamTypeDescription | |
| last_hidden_stateTensorTensor of shape [batchSize, seqLength, embedDim] | |
| attention_maskTensorTensor of shape [batchSize, seqLength] | |
| * * * | |
| ## `utils/tensor.layer_norm(input, normalized_shape, options)` ⇒ [Tensor](#Tensor) | |
| Apply Layer Normalization for last certain number of dimensions. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The normalized tensor. | |
| ParamTypeDefaultDescription | |
| inputTensorThe input tensor | |
| normalized_shapeArrayinput shape from an expected input of size | |
| optionsObjectThe options for the layer normalization | |
| [options.eps]number1e-5A value added to the denominator for numerical stability. | |
| * * * | |
| ## `utils/tensor.cat(tensors, dim)` ⇒ [Tensor](#Tensor) | |
| Concatenates an array of tensors along a specified dimension. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The concatenated tensor. | |
| ParamTypeDescription | |
| tensorsArrayThe array of tensors to concatenate. | |
| dimnumberThe dimension to concatenate along. | |
| * * * | |
| ## `utils/tensor.stack(tensors, dim)` ⇒ [Tensor](#Tensor) | |
| Stack an array of tensors along a specified dimension. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The stacked tensor. | |
| ParamTypeDescription | |
| tensorsArrayThe array of tensors to stack. | |
| dimnumberThe dimension to stack along. | |
| * * * | |
| ## `utils/tensor.std_mean(input, dim, correction, keepdim)` ⇒ Array | |
| 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. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: Array - A tuple of (std, mean) tensors. | |
| ParamTypeDescription | |
| inputTensorthe input tenso | |
| dimnumber | nullthe dimension to reduce. If None, all dimensions are reduced. | |
| correctionnumberdifference between the sample size and sample degrees of freedom. Defaults to Bessel's correction, correction=1. | |
| keepdimbooleanwhether the output tensor has dim retained or not. | |
| * * * | |
| ## `utils/tensor.mean(input, dim, keepdim)` ⇒ [Tensor](#Tensor) | |
| Returns the mean value of each row of the input tensor in the given dimension dim. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - A new tensor with means taken along the specified dimension. | |
| ParamTypeDescription | |
| inputTensorthe input tensor. | |
| dimnumber | nullthe dimension to reduce. | |
| keepdimbooleanwhether the output tensor has dim retained or not. | |
| * * * | |
| ## `utils/tensor.full(size, fill_value)` ⇒ [Tensor](#Tensor) | |
| Creates a tensor of size size filled with fill_value. The tensor's dtype is inferred from fill_value. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The filled tensor. | |
| ParamTypeDescription | |
| sizeArrayA sequence of integers defining the shape of the output tensor. | |
| fill_valuenumber | bigint | booleanThe value to fill the output tensor with. | |
| * * * | |
| ## `utils/tensor.ones(size)` ⇒ [Tensor](#Tensor) | |
| Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument size. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The ones tensor. | |
| ParamTypeDescription | |
| sizeArrayA sequence of integers defining the shape of the output tensor. | |
| * * * | |
| ## `utils/tensor.ones_like(tensor)` ⇒ [Tensor](#Tensor) | |
| Returns a tensor filled with the scalar value 1, with the same size as input. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The ones tensor. | |
| ParamTypeDescription | |
| tensorTensorThe size of input will determine size of the output tensor. | |
| * * * | |
| ## `utils/tensor.zeros(size)` ⇒ [Tensor](#Tensor) | |
| Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument size. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The zeros tensor. | |
| ParamTypeDescription | |
| sizeArrayA sequence of integers defining the shape of the output tensor. | |
| * * * | |
| ## `utils/tensor.zeros_like(tensor)` ⇒ [Tensor](#Tensor) | |
| Returns a tensor filled with the scalar value 0, with the same size as input. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The zeros tensor. | |
| ParamTypeDescription | |
| tensorTensorThe size of input will determine size of the output tensor. | |
| * * * | |
| ## `utils/tensor.rand(size)` ⇒ [Tensor](#Tensor) | |
| Returns a tensor filled with random numbers from a uniform distribution on the interval [0, 1) | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The random tensor. | |
| ParamTypeDescription | |
| sizeArrayA sequence of integers defining the shape of the output tensor. | |
| * * * | |
| ## `utils/tensor.randn(size)` ⇒ [Tensor](#Tensor) | |
| Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution). | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The random tensor. | |
| ParamTypeDescription | |
| sizeArrayA sequence of integers defining the shape of the output tensor. | |
| * * * | |
| ## `utils/tensor.quantize_embeddings(tensor, precision)` ⇒ [Tensor](#Tensor) | |
| Quantizes the embeddings tensor to binary or unsigned binary precision. | |
| **Kind**: static method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: [Tensor](#Tensor) - The quantized tensor. | |
| ParamTypeDescription | |
| tensorTensorThe tensor to quantize. | |
| precision'binary' | 'ubinary'The precision to use for quantization. | |
| * * * | |
| ## `utils/tensor~args[0]` : ONNXTensor | |
| **Kind**: inner property of [utils/tensor](#module_utils/tensor) | |
| * * * | |
| ## `utils/tensor~reshape(data, dimensions)` ⇒ NestArray.<T, DIM> | |
| Reshapes a 1-dimensional array into an n-dimensional array, according to the provided dimensions. | |
| **Kind**: inner method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: NestArray.<T, DIM> - The reshaped array. | |
| ParamTypeDescription | |
| dataArray | DataArrayThe input array to reshape. | |
| dimensionsDIMThe target shape/dimensions. | |
| **Example** | |
| ```js | |
| reshape([10 ], [1 ]); // Type: number[] Value: [10] | |
| reshape([1, 2, 3, 4 ], [2, 2 ]); // Type: number[][] Value: [[1, 2], [3, 4]] | |
| reshape([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]); // Type: number[][][] Value: [[[1, 2], [3, 4]], [[5, 6], [7, 8]]] | |
| reshape([1, 2, 3, 4, 5, 6, 7, 8], [4, 2 ]); // Type: number[][] Value: [[1, 2], [3, 4], [5, 6], [7, 8]] | |
| ``` | |
| * * * | |
| ### `reshape~reshapedArray` : any | |
| **Kind**: inner property of [reshape](#module_utils/tensor..reshape) | |
| * * * | |
| ## `utils/tensor~reduce_helper(callbackfn, input, dim, keepdim, [initialValue])` ⇒ Array | |
| **Kind**: inner method of [utils/tensor](#module_utils/tensor) | |
| **Returns**: Array - The reduced tensor data. | |
| ParamTypeDefaultDescription | |
| callbackfnfunction | |
| inputTensorthe input tensor. | |
| dimnumberthe dimension to reduce. | |
| keepdimbooleanfalsewhether the output tensor has dim retained or not. | |
| [initialValue]anythe initial value to start the reduction with. | |
| * * * | |
| ## `utils/tensor~DataArray` : string | |
| **Kind**: inner typedef of [utils/tensor](#module_utils/tensor) | |
| * * * | |
| ## `utils/tensor~NestArray` : any | |
| This creates a nested array of a given type and depth (see examples). | |
| **Kind**: inner typedef of [utils/tensor](#module_utils/tensor) | |
| **Example** | |
| ```js | |
| NestArray; // string[] | |
| ``` | |
| **Example** | |
| ```js | |
| NestArray; // number[][] | |
| ``` | |
| **Example** | |
| ```js | |
| NestArray; // string[][][] etc. | |
| ``` | |
| * * * | |
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