torch.linalg.eigvals
torch.linalg.eigvals(A, *, out=None) -> Tensor
Computes the eigenvalues of a square matrix.
Letting \mathbb{K} be \mathbb{R} or \mathbb{C}, the eigenvalues
   of a square matrix A \in \mathbb{K}^{n \times n} are defined as the
   roots (counted with multiplicity) of the polynomial p of degree
   n given by
  p(\lambda) = \operatorname{det}(A - \lambda
  \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{C}}

where \mathrm{I}_n is the n-dimensional identity matrix.
Supports input of float, double, cfloat and cdouble dtypes. Also
   supports batches of matrices, and if "A" is a batch of matrices
   then the output has the same batch dimensions.
Note:
 The eigenvalues of a real matrix may be complex, as the roots of
 a real polynomial may be complex.The eigenvalues of a matrix are
 always well-defined, even when the matrix is not diagonalizable.

Note: