// CHECK:  %[[MUL3:.*]] = "tf.Mul"(%[[MEAN]], %[[MUL1]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  // CHECK:  %[[SUB:.*]] = "tf.Sub"(%arg2, %[[MUL3]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  // CHECK:  %[[ADD0:.*]] = "tf.Add"(%[[MUL2]], %[[SUB]]) : (tensor<1x1x6x2xf32>, tensor<2xf32>) -> tensor<1x1x6x2xf32>

//CHECK:func.func private @slice(%arg0: tensor<160x20x1xf32>) -> tensor<1x1x1xf32> {
//CHECK-NEXT: %0 = "vhlo.slice_v1"(%arg0) <{limit_indices = #vhlo.tensor_v1<dense<0> : tensor<3xi64>>, start_indices = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>, strides = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>}> : (tensor<160x20x1xf32>) -> tensor<1x1x1xf32> 
//CHECK-NEXT: return %0 : tensor<1x1x1xf32>
//CHECK-NEXT:}

// CHECK:           %[[VAL_7:.*]] = "tfl.concatenation"(%[[VAL_5]], %[[VAL_6]]) <{axis = 3 : i32, fused_activation_function = "NONE"}> : (tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x2xf32>
// CHECK:           %[[VAL_8:.*]] = "tfl.reshape"(%[[VAL_7]], %[[VAL_3]]) : (tensor<1x1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32>
// CHECK:           %[[VAL_9:.*]] = "tfl.reshape"(%[[VAL_8]], %[[VAL_4]]) : (tensor<2xf32>, tensor<2xi32>) -> tensor<2x1xf32>

    %3 = "tfl.reshape"(%2, %cst_0) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32>
    %4 = "tfl.reshape"(%3, %cst_1) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<2xf32>, tensor<2xi32>) -> tensor<2x1xf32>

  %cst = "tf.Const"() {value = dense<1.1> : tensor<100x512xf32>} : () -> tensor<100x512xf32>
  %one = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

  %21 = "tf.Const"() {device = "", name = "Const_143", dtype = "tfdtype$DT_FLOAT", value = dense<0.24288677062973696> : tensor<1x1x6x2xf32>} : () -> tensor<1x1x6x2xf32>
  // CHECK-DAG: value = #tf_type<tensor_proto
  // CHECK-DAG: tf.Const{{.*}} dense<0.242886767> : tensor<1x1x6x2xf32>
  func.return %0, %21 : tensor<4xf32>, tensor<1x1x6x2xf32>
}

// CHECK-LABEL: func @testAdd() -> tensor<2x2xi32>
func.func @testAdd() -> tensor<2x2xi32> {
^bb0:

func.func @NotQuantizeBatchMatmulWithConstAct(%arg0: tensor<1x1x3x512xf32>) -> tensor<1x1x12x3xf32> {
  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 1.000000e+01]> : tensor<2xf32>} : (tensor<1x1x3x512xf32>) -> tensor<1x1x3x512xf32>
  %w = arith.constant dense<127.0> : tensor<1x1x12x512xf32>
  %mm = "tfl.batch_matmul"(%w, %0) {adj_x = false, adj_y = true} : (tensor<1x1x12x512xf32>, tensor<1x1x3x512xf32>) -> tensor<1x1x12x3xf32>

// CHECK-LABEL: squeezeToReshape
func.func @squeezeToReshape(%arg0: tensor<1x1x2xf32>) -> tensor<2xf32> {
  %0 = "tfl.squeeze"(%arg0) : (tensor<1x1x2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>

  // CHECK-DAG: [[CONST:.*]] = arith.constant dense<2> : tensor<1xi32>
  // CHECK: %[[RESULT:.*]] = "tfl.reshape"(%arg0, %[[CONST:.*]]) : (tensor<1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32>
  // CHECK: return %[[RESULT]]
}

// CHECK-LABEL: func @tfAssertTrue
func.func @tfAssertTrue(%arg0: tensor<1x1x6x2xf32>) {
  %t = arith.constant dense<true> : tensor<i1>
  // CHECK-NOT: tf.Assert
  "tf.Assert"(%t, %arg0) {summarize = 3} : (tensor<i1>, tensor<1x1x6x2xf32>) -> ()
  func.return
}

// CHECK-LABEL: func @tfAssertFalse
func.func @tfAssertFalse(%arg0: tensor<1x1x6x2xf32>) {
  %f = arith.constant dense<false> : tensor<i1>
  // CHECK: tf.Assert

func.func @testPReluValidBroadcast(%arg0: tensor<19x7x12x14xf32>, %arg1: tensor<1x1x14xf32>) -> tensor<19x7x12x14xf32> {
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<19x7x12x14xf32>, tensor<1x1x14xf32>) -> tensor<19x7x12x14xf32>
  func.return %0 : tensor<19x7x12x14xf32>
}

// -----

func.func @testPReluValidFullBroadcast(%arg0: tensor<7x8x9x10xf32>, %arg1: tensor<1x1x1xf32>) -> tensor<7x8x9x10xf32> {