Update the docs of torch.eig about derivative

torch/_torch_docs.py

@@ -2640,7 +2640,7 @@ def merge_dicts(*dicts):
 differently than dot(a, b). If the first argument is complex, the complex conjugate of the
 first argument is used for the calculation of the dot product.
 
-.. note:: 
+.. note::
 
     Unlike NumPy's vdot, torch.vdot intentionally only supports computing the dot product
     of two 1D tensors with the same number of elements.
@@ -2672,7 +2672,7 @@ def merge_dicts(*dicts):
 
 .. note::
     Since eigenvalues and eigenvectors might be complex, backward pass is supported only
-    for :func:`torch.symeig`
+    if eigenvalues and eigenvectors are all real valued.
 
 Args:
     input (Tensor): the square matrix of shape :math:`(n \times n)` for which the eigenvalues and eigenvectors