Add jsdoc comments with Typedoc

[vtempest]

Describe the Feature

I highly suggest it and I want to colab together on https://airesearch.wiki

/**
 * JS-PyTorch is a Deep Learning library with GPU.js acceleration in PyTorch API syntax.
 * @author [JS-PyTorch](https://eduardoleao052.github.io/js-pytorch/site/index.html)
 * @module torch
 * 
 * Tensor Creation and Manipulation:
 * @function tensor(data, requires_grad = false, device = 'cpu') Creates a new Tensor filled with the given data
 * @function zeros(*shape, requires_grad = false, device = 'cpu') Creates a new Tensor filled with zeros
 * @function ones(*shape, requires_grad = false, device = 'cpu') Creates a new Tensor filled with ones
 * @function tril(*shape, requires_grad = false, device = 'cpu') Creates a new 2D lower triangular Tensor
 * @function randn(*shape, requires_grad = false, device = 'cpu', xavier = false) Creates a new Tensor filled with random values from a normal distribution
 * @function rand(*shape, requires_grad = false, device = 'cpu') Creates a new Tensor filled with random values from a uniform distribution
 * @function randint(low, high, *shape, requires_grad = false, device = 'cpu') Creates a new Tensor filled with random integers
 * 
 * Tensor Methods:
 * @method tensor.backward() Performs backpropagation from this tensor backwards
 * @method tensor.zero_grad() Clears the gradients stored in this tensor
 * @method tensor.zero_grad_graph() Clears the gradients stored in this tensor and all tensors that led to it
 * @method tensor.tolist() Returns the tensor's data as a JavaScript Array
 * @property tensor.data Returns the tensor's data as a JavaScript Array
 * @property tensor.length Returns the tensor's length (size of first dimension)
 * @property tensor.ndims Returns the number of dimensions in the Tensor
 * @property tensor.grad Returns the gradients currently stored in the Tensor
 * 
 * Tensor Operations:
 * @function add(a, b) Performs element-wise addition of two tensors
 * @function sub(a, b) Performs element-wise subtraction of two tensors
 * @function neg(a) Returns the element-wise opposite of the given Tensor
 * @function mul(a, b) Performs element-wise multiplication of two tensors
 * @function div(a, b) Performs element-wise division of two tensors
 * @function matmul(a, b) Performs matrix multiplication between two tensors
 * @function sum(a, dim, keepdims = false) Gets the sum of the Tensor over a specified dimension
 * @function mean(a, dim, keepdims = false) Gets the mean of the Tensor over a specified dimension
 * @function variance(a, dim, keepdims = false) Gets the variance of the Tensor over a specified dimension
 * @function transpose(a, dim1, dim2) Transposes the tensor along two consecutive dimensions
 * @function at(a, index1, index2) Returns elements from the tensor based on given indices
 * @function masked_fill(a, condition, value) Fills elements in the tensor based on a condition
 * @function pow(a, n) Returns tensor raised to element-wise power
 * @function sqrt(a) Returns element-wise square root of the tensor
 * @function exp(a) Returns element-wise exponentiation of the tensor
 * @function log(a) Returns element-wise natural log of the tensor
 * 
 * Neural Network Layers:
 * @class nn.Linear(in_size, out_size, device, bias, xavier) Applies a linear transformation to the input tensor
 * @class nn.MultiHeadSelfAttention(in_size, out_size, n_heads, n_timesteps, dropout_prob, device) Applies a self-attention layer on the input tensor
 * @class nn.FullyConnected(in_size, out_size, dropout_prob, device, bias) Applies a fully-connected layer on the input tensor
 * @class nn.Block(in_size, out_size, n_heads, n_timesteps, dropout_prob, device) Applies a transformer Block layer on the input tensor
 * @class nn.Embedding(in_size, embed_size) Creates an embedding table for vocabulary
 * @class nn.PositionalEmbedding(input_size, embed_size) Creates a positional embedding table
 * @class nn.ReLU() Applies Rectified Linear Unit activation function
 * @class nn.Softmax() Applies Softmax activation function
 * @class nn.Dropout(drop_prob) Applies dropout to input tensor
 * @class nn.LayerNorm(n_embed) Applies Layer Normalization to input tensor
 * @class nn.CrossEntropyLoss() Computes Cross Entropy Loss between target and input tensor
 * 
 * Optimization:
 * @class optim.Adam(params, lr, reg, betas, eps) Adam optimizer for updating model parameters
 * 
 * Utility Functions:
 * @function save(model, file) Saves the model reruning data blob (for you to save)
 * @function load(model, loadedData) Loads the model from saved data
 */

Motivation [optional]

No response

Sample Code

No response

Would you like to work on this issue?

None