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# torch.linalg.inv_ex

torch.linalg.inv_ex(A, *, check_errors=False, out=None)

  Computes the inverse of a square matrix if it is invertible.

  Returns a namedtuple `(inverse, info)`. `inverse` contains the result of inverting `A` and `info` stores the LAPACK error codes.

  If `A` is not an invertible matrix, or if it’s a batch of matrices and one or more of them is not an invertible matrix, then `info` stores a positive integer for the corresponding matrix. The positive integer indicates the diagonal element of the LU decomposition of the input matrix that is exactly zero. `info` filled with zeros indicates that the inversion was successful. If `check_errors=True` and `info` contains positive integers, then a RuntimeError is thrown.

  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

  When the inputs are on a CUDA device, this function synchronizes only when `check_errors`= True.

  Warning

  This function is “experimental” and it may change in a future PyTorch release.

  See also

  `torch.linalg.inv()` is a NumPy compatible variant that always checks for errors.

  Parameters:

    - A (Tensor) – tensor of shape (*, n, n) where * is zero or more batch dimensions consisting of square matrices.

    - check_errors (bool, optional) – controls whether to check the content of `info`. Default: False.

  Keyword Arguments:

    out (tuple, optional) – tuple of two tensors to write the output to. Ignored if None. Default: None.

  Examples:

      >>> A = torch.randn(3, 3)
      >>> Ainv, info = torch.linalg.inv_ex(A)
      >>> torch.dist(torch.linalg.inv(A), Ainv)
      tensor(0.)
      >>> info
      tensor(0, dtype=torch.int32)

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