// Ensure basic_lstm roundtrip exactly

func.func @main(%arg0: tensor<1x384xf32>, %arg1: tensor<1x96xf32>, %arg2: tensor<384x480xf32>, %arg3: tensor<384xf32>, %arg4: tensor<1x96xf32>) -> tensor<1x96xf32> {
// CHECK-LABEL: @main

  %7 = "tf.AddV2"(%arg4, %6) {T = f32, device = ""} : (tensor<*xf32>, tensor<?x?xf32>) -> tensor<*xf32>
  %8 = "tf.AddV2"(%arg0, %cst_2) {T = i32, device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  func.return %8, %arg1, %0, %arg3, %7 : tensor<*xi32>, tensor<*xi32>, tensor<*xi32>, tensor<*xf32>, tensor<*xf32>
}

// CHECK-LABEL: func @transpose_resnet_layer
func.func @transpose_resnet_layer(%arg0: tensor<?x224x224x3xf32>, // input
                             %arg1: tensor<64xf32>,          // batch_norm args
                             %arg2: tensor<256xf32>,          // batch_norm args
                             %arg3: tensor<7x7x3x64xf32>,    // conv filter #0
                             %arg4: tensor<1x1x64x256xf32>   // conv filter #1

// CHECK-NEXT:  buffer: 10
// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

^bb0(%arg0: tensor<1x384xf32>, %arg1: tensor<1x96xf32>, %arg2: tensor<384x480xf32>, %arg3: tensor<384xf32>, %arg4: tensor<1x96xf32>):

// CHECK-LABEL: func @quantized_types
func.func @quantized_types(%arg0: tensor<!tf_type.resource<tensor<1x3x!tf_type.quint8>>>,
                      %arg1: tensor<!tf_type.resource<tensor<1x3x!tf_type.quint16>>>,
                      %arg2: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint8>>>,
                      %arg3: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint16>>>,
                      %arg4: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint32>>>) {

^bb0(%arg0: tensor<8xf32>, %arg3: tensor<f32>, %arg4: tensor<f32>):
  %1 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg3, %arg4) {num_bits = 5, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %1 : tensor<8xf32>

// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK: return %0 : tensor<8xf32>
}

# CHECK-SAME: attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "arg0,arg1,arg2", outputs = "ret0,ret1,ret2"}}...

// CHECK:       tf.OpC
func.func @result_used_in_cond(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):
    %1 = "tf.OpA"(%arg2) {is_stateless = true} : (tensor<*xf32>) -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):

^bb0(%arg0: tensor<8xf32>, %arg3: tensor<f32>, %arg4: tensor<f32>):
  %1 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg3, %arg4) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %1 : tensor<8xf32>

// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK: return %0 : tensor<8xf32>
}

func.func @op_with_region_mixed_tfl_shlo_tfl(%arg0: tensor<7x5xf32>, %arg1 : tensor<5xf32>) -> tensor<5xf32> {
  %0 = "stablehlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<5xf32>, %arg3: tensor<5xf32>):
    // CHECK:      %1 = "tfl.abs"(%arg2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32>
    // CHECK-NEXT: %2 = "vhlo.add_v1"(%1, %arg2) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>