Creates a criterion that measures the loss given input tensors
\(x_1\), \(x_2\) and a `Tensor`

label \(y\) with values 1 or -1.
This is used for measuring whether two inputs are similar or dissimilar,
using the cosine distance, and is typically used for learning nonlinear
embeddings or semi-supervised learning.
The loss function for each sample is:

## Arguments

- margin
(float, optional): Should be a number from \(-1\) to \(1\), \(0\) to \(0.5\) is suggested. If

`margin`

is missing, the default value is \(0\).- reduction
(string, optional): Specifies the reduction to apply to the output:

`'none'`

|`'mean'`

|`'sum'`

.`'none'`

: no reduction will be applied,`'mean'`

: the sum of the output will be divided by the number of elements in the output,`'sum'`

: the output will be summed.