%0 = arith.constant dense<-1> : tensor<1xi32>
  %1 = "tfl.reduce_max"(%arg0, %0) {keep_dims = true} : (tensor<10x10xf32>, tensor<1xi32>) -> tensor<10x1xf32>
  %2 = tfl.sub(%arg0, %1) {fused_activation_function = "NONE"} : (tensor<10x10xf32>, tensor<10x1xf32>) -> tensor<10x10xf32>
  %3 = "tfl.exp"(%2) : (tensor<10x10xf32>) -> tensor<10x10xf32>

    // CHECK-NEXT: return %[[DIV]] : tensor<4x10xf32>
    %0 = "tf.Softsign"(%arg0) : (tensor<4x10xf32>) -> tensor<4x10xf32>
    func.return %0 : tensor<4x10xf32>
}

// -----

// CHECK-LABEL: func @softsign_grad
func.func @softsign_grad(%arg0: tensor<4x10xf32>, %arg1: tensor<4x10xf32>) -> tensor<4x10xf32> {

    // CHECK-NEXT: %[[ONE:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>

// CHECK:           }) : (tensor<5x10xf32>, tensor<3x1xi32>, tensor<10x3xf32>) -> tensor<5x10xf32>
// CHECK:           return %[[VAL_3]] : tensor<5x10xf32>
// CHECK:         }
func.func @convert_scatter_update_with_non_trailing_update_window_dims(
  %arg0: tensor<5x10xf32>,
  %arg1: tensor<3x1xi32>,
  %arg2: tensor<10x3xf32>) -> tensor<5x10xf32>
{
  %0 = "mhlo.scatter"(%arg0, %arg1, %arg2) ({