%0 = "tf.Softplus"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32>

  // CHECK:     return [[ENTRY_SELECT]] : tensor<8x16xf32>
  func.return %0 : tensor<8x16xf32>
}

// -----

// CHECK-LABEL: func @softplus_f64
// CHECK-SAME: ([[FEATURES:%.*]]: tensor<8x16xf64>)
func.func @softplus_f64(%arg0: tensor<8x16xf64>) -> tensor<8x16xf64> {

  %2 = "tfl.exp"(%1) : (tensor<8x128xf32>) -> tensor<8x128xf32>
  %3 = "tfl.sum"(%2, %cst) {keep_dims = true} : (tensor<8x128xf32>, tensor<1xi32>) -> tensor<8x1xf32>
  %4 = "tfl.div"(%2, %3) {fused_activation_function = "NONE"} : (tensor<8x128xf32>, tensor<8x1xf32>) -> tensor<8x128xf32>
  func.return %4 : tensor<8x128xf32>

// CHECK-LABEL: SoftMaxWithNormalization

//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x1xf32>
// %1 = "mhlo.slice"(%input) {
//        limit_indices = dense<[4, 3]> : tensor<2xi64>,
//        start_indices = dense<[0, 1]> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>
// %2 = "mhlo.slice"(%input) {