Fix for ambiguity in linalg.norm()'s ord argument of +2 & -2 (pytorch#155148)

Fixes pytorch#136453

### Description
---
Fixed the ambiguity by referencing a hyperlink to wikipedia's SVD/Singular Values section as per past discussion (by other contributors) on the above thread.

In the ord argument, for values `+2` and `-2`, the `singular value` now points to [this section of singular values on the wiki SVD page](https://en.wikipedia.org/wiki/Singular_value_decomposition#Singular_values,_singular_vectors,_and_their_relation_to_the_SVD).

### Why not mention SVD
---
For conciseness (expanding 'largest singular value' -> 'largest singular value of a SVD' is too much, i think, wrt rest of the table)

---

I hope this is satisfactory. Please let me know if I have missed anything essential; cheers.

Pull Request resolved: pytorch#155148
Approved by: https://github.com/Skylion007, https://github.com/lezcano

Author: ANotFox

torch/linalg/__init__.py

@@ -1369,21 +1369,21 @@
 
 :attr:`ord` defines the norm that is computed. The following norms are supported:
 
-======================     =========================  ========================================================
-:attr:`ord`                norm for matrices          norm for vectors
-======================     =========================  ========================================================
-`None` (default)           Frobenius norm             `2`-norm (see below)
-`'fro'`                    Frobenius norm             -- not supported --
-`'nuc'`                    nuclear norm               -- not supported --
-`inf`                      `max(sum(abs(x), dim=1))`  `max(abs(x))`
-`-inf`                     `min(sum(abs(x), dim=1))`  `min(abs(x))`
-`0`                        -- not supported --        `sum(x != 0)`
-`1`                        `max(sum(abs(x), dim=0))`  as below
-`-1`                       `min(sum(abs(x), dim=0))`  as below
-`2`                        largest singular value     as below
-`-2`                       smallest singular value    as below
-other `int` or `float`     -- not supported --        `sum(abs(x)^{ord})^{(1 / ord)}`
-======================     =========================  ========================================================
+======================     ==========================   ======================================================
+:attr:`ord`                norm for matrices            norm for vectors
+======================     ==========================   ======================================================
+`None` (default)           Frobenius norm               `2`-norm (see below)
+`'fro'`                    Frobenius norm               -- not supported --
+`'nuc'`                    nuclear norm                 -- not supported --
+`inf`                      `max(sum(abs(x), dim=1))`    `max(abs(x))`
+`-inf`                     `min(sum(abs(x), dim=1))`    `min(abs(x))`
+`0`                        -- not supported --          `sum(x != 0)`
+`1`                        `max(sum(abs(x), dim=0))`    as below
+`-1`                       `min(sum(abs(x), dim=0))`    as below
+`2`                        largest `singular value`_    as below
+`-2`                       smallest `singular value`_   as below
+other `int` or `float`     -- not supported --          `sum(abs(x)^{ord})^{(1 / ord)}`
+======================     ==========================   ======================================================
 
 where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
 
@@ -1483,6 +1483,9 @@
     tensor([ 3.7417, 11.2250])
     >>> LA.norm(A[0, :, :]), LA.norm(A[1, :, :])
     (tensor(3.7417), tensor(11.2250))
+
+.. _singular value:
+    https://en.wikipedia.org/wiki/Singular_value_decomposition#Singular_values,_singular_vectors,_and_their_relation_to_the_SVD
 """,
 )