message: Auto generated TensorFlow SelfAdjointEigV2 op
diff: @@ -8266,6 +8266,39 @@ def TF_SelectV2Op : TF_Op<"SelectV2", [NoSideEffect, ResultsBroadcastableShape]>
  ];
  }
  +def TF_SelfAdjointEigV2Op : TF_Op<"SelfAdjointEigV2", [NoSideEffect]> {
  + let summary = [{
  +Computes the eigen decomposition of one or more square self-adjoint matrices.
  + }];
  +
  + let description = [{
  +Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
  +`input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues
  +are sorted in non-decreasing order.
  +
  +```python
  +# a is a tensor.
  +# e is a tensor of eigenvalues.
  +# v is a tensor of eigenvectors.
  +e, v = self_adjoint_eig(a)
  +e = self_adjoint_eig(a, compute_v=False)
  +```
  + }];
  +
  + let arguments = (ins
  + TensorOf<[F16, F32, F64, TF_Complex128, TF_Complex64]>:$input,
  +
  + DefaultValuedAttr<BoolAttr, "true">:$compute_v
  + );
  +
  + let results = (outs
  + TensorOf<[F16, F32, F64, TF_Complex128, TF_Complex64]>:$e,
  + TensorOf<[F16, F32, F64, TF_Complex128, TF_Complex64]>:$v
  + );
  +
  + TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
  +}
  +
  def TF_SeluOp : TF_Op<"Selu", [NoSideEffect, SameOperandsAndResultType]> {
  let summary = [{
  Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
  