BroadcastGradientArgs 4
 ConcatOffset 1
 ConcatV2 14
 Const 199
 DynamicStitch 1
 FloorDiv 1
 MatMul 20
 Maximum 1
 Mul 74
 NoOp 13
 Reshape 86
 RsqrtGrad 1
 Select 36
 SigmoidGrad 24
 Slice 32
 StridedSlice 2
 Sub 1
 Sum 11
 TanhGrad 17
 Tile 2
 ZerosLike 1
cluster 16 size 22
 Add 2
 BiasAdd 1
 ConcatV2 1
 Const 3
 GreaterEqual 1
 MatMul 1
 Mul 3

  // evaluated.
  Scope grad_scope = scope.WithControlDependencies(grad);
  auto y = ConjugateHelper(grad_scope, op.output(0));
  grad_outputs->push_back(internal::SigmoidGrad(grad_scope, y, grad));
  return grad_scope.status();
}
REGISTER_GRADIENT_OP("Sigmoid", SigmoidGrad);

Status SignGrad(const Scope& scope, const Operation& op,
                const std::vector<Output>& grad_inputs,

// =============================================================================

func.func @sigmoidGrad(%arg0: tensor<?x32xf32>, %arg1: tensor<?x32xf32>) -> tensor<?x32xf32> {
  %0 = "tf.SigmoidGrad"(%arg0, %arg1) : (tensor<?x32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>
  func.return %0 : tensor<?x32xf32>
// CHECK-LABEL: sigmoidGrad
// CHECK-NEXT: [[ONE:%.+]] = arith.constant dense<1.000000e+00> : tensor<f32>

            "ReciprocalGrad", "RsqrtGrad", "SqrtGrad", "TruncateDiv",
            "TruncateMod", "Equal", "NotEqual", "Greater", "GreaterEqual",
            "Less", "LessEqual", "SigmoidGrad", "SoftplusGrad", "SoftsignGrad",
            "TanhGrad", "Pow", "SquaredDifference", "ApproximateEqual",
            // Others
            "AddN", "Bitcast", "Cast", "ClipByValue", "Const", "Empty",

  // CHECK-DAG: [[SUB:%.+]] =  mhlo.subtract [[ONE]], %arg0 : tensor<2xf32>
  // CHECK-DAG: [[MUL1:%.+]] =  mhlo.multiply [[MUL0]], [[SUB]] : tensor<2xf32>
  // CHECK: return [[MUL1]]
  %0 = "tf.SigmoidGrad"(%arg0, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}

// -----

// CHECK-LABEL: @sigmoid_grad_complex

    static bool isCompatibleReturnTypes(TypeRange inferred, TypeRange actual) {
      return ArraysAreCastCompatible(inferred, actual);
    }
  }];
}

def TF_SigmoidGradOp : TF_Op<"SigmoidGrad", [Pure, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes the gradient of the sigmoid of `x` wrt its input.";

  let description = [{