* Add tensors or tensor and integers. * @param {any} a - First tensor or integer. * @param {any} b - Second tensor or integer. * @returns {ob

* Get element-wise opposite of given tensor ( every element * (-1) ) * @param {object} a - Tensor to be multiplied by -1. * @returns {object} Ne

* Perform element-wise multiplication between Tensors and integers or other Tensors. * @param {any} a - First tensor or integer. * @param {any}

* Get element-wise square root of given tensor * @param {object} a - Tensor to be square rooted. * @returns {object} New tensor.

* Get element-wise exponentiation of given tensor ( e^(every element) ) * @param {object} a - Tensor to be exponentiated. * @returns {object} Ne

* Get element-wise natural log of given tensor ( ln(every element) ) * @param {object} a - Tensor we will take the log of. * @returns {object} N

* Gets the sum of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to sum. * @param {dim} dim - Dimension to sum over. * @pa

* Gets the mean of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to get mean from. * @param {dim} dim - Dimension to get mea

* Gets the variance of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to get variance of. * @param {dim} dim - Dimension to g

* Transpose the tensor along two consecutive dimensions: * @param {object} a - Tensor to transpose. * @param {number} dim1 - First dimension.

* Performs forward pass through MSELoss, returns loss. * @param {Tensor} z - Output from the last layer of the network. * @param {object} y - Co

* Add tensors or tensor and integers. * @param {any} a - First tensor or integer. * @param {any} b - Second tensor or integer. * @returns {ob

* Get element-wise opposite of given tensor ( every element * (-1) ) * @param {object} a - Tensor to be multiplied by -1. * @returns {object} Ne

* Perform element-wise multiplication between Tensors and integers or other Tensors. * @param {any} a - First tensor or integer. * @param {any}

* Get element-wise square root of given tensor * @param {object} a - Tensor to be square rooted. * @returns {object} New tensor.

* Get element-wise exponentiation of given tensor ( e^(every element) ) * @param {object} a - Tensor to be exponentiated. * @returns {object} Ne

* Get element-wise natural log of given tensor ( ln(every element) ) * @param {object} a - Tensor we will take the log of. * @returns {object} N

* Gets the sum of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to sum. * @param {dim} dim - Dimension to sum over. * @pa

* Gets the mean of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to get mean from. * @param {dim} dim - Dimension to get mea

* Gets the variance of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to get variance of. * @param {dim} dim - Dimension to g

* Transpose the tensor along two consecutive dimensions: * @param {object} a - Tensor to transpose. * @param {number} dim1 - First dimension.

* Add tensors or tensor and integers. * @param {any} a - First tensor or integer. * @param {any} b - Second tensor or integer. * @returns {ob

* Get element-wise opposite of given tensor ( every element * (-1) ) * @param {object} a - Tensor to be multiplied by -1. * @returns {object} Ne

* Perform element-wise multiplication between Tensors and integers or other Tensors. * @param {any} a - First tensor or integer. * @param {any}

* Get element-wise square root of given tensor * @param {object} a - Tensor to be square rooted. * @returns {object} New tensor.

* Get element-wise exponentiation of given tensor ( e^(every element) ) * @param {object} a - Tensor to be exponentiated. * @returns {object} Ne

* Get element-wise natural log of given tensor ( ln(every element) ) * @param {object} a - Tensor we will take the log of. * @returns {object} N

* Gets the sum of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to sum. * @param {dim} dim - Dimension to sum over. * @pa

* Gets the mean of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to get mean from. * @param {dim} dim - Dimension to get mea

* Gets the variance of a Tensor over a specified dimension. * @param {Tensor} a - Tensor to get variance of. * @param {dim} dim - Dimension to g

* Transpose the tensor along two consecutive dimensions: * @param {object} a - Tensor to transpose. * @param {number} dim1 - First dimension.