func.return %4 : tensor<8x128xf32>

// CHECK-LABEL: SoftMaxWithNormalization
// CHECK: %[[RESULT:.*]] = "tfl.softmax"(%arg0) <{beta = 1.000000e+00 : f32}> : (tensor<8x128xf32>) -> tensor<8x128xf32>
// CHECK: return %[[RESULT]] : tensor<8x128xf32>
}

func.func @SoftMaxWithoutNormalization(%arg0: tensor<8x128xf32>) -> tensor<8x128xf32> {

// CHECK-LABEL: topk_v2
// CHECK-SAME: %[[INPUT:.*]]: tensor<16x16xf32>
func.func @topk_v2(%input: tensor<16x16xf32>) -> (tensor<16x8xf32>, tensor<16x8xi32>) {
  %k = "tf.Const"() {value = dense<8> : tensor<i32>} : () -> tensor<i32>

  // CHECK:     chlo.top_k(%[[INPUT]], k = 8)
  %0:2 = "tf.TopKV2"(%input, %k): (tensor<16x16xf32>, tensor<i32>) -> (tensor<16x8xf32>, tensor<16x8xi32>)

// CHECK:           %[[VAL_6:.*]] = "tf.Slice"(%[[VAL_3]], %[[VAL_4]], %[[VAL_5]]) : (tensor<8x129xf32>, tensor<2xi64>, tensor<2xi64>) -> tensor<7x128xf32>
// CHECK:           return %[[VAL_6]] : tensor<7x128xf32>
// CHECK:         }
func.func @convert_pad_negative_amount(%arg0: tensor<8x128xf32>, %arg1: tensor<f32>) -> tensor<7x128xf32> {
  %0 = "mhlo.pad"(%arg0, %arg1) {
    edge_padding_low = dense<[0, -1]> : tensor<2xi64>,