// CHECK-LABEL: func @single_restore_group
// CHECK-SAME:    (%[[ARG0:.*]]: {{.*}})
func.func @single_restore_group(%arg0: tensor<!tf_type.string>) -> (tensor<*xf32>, tensor<*xi32>) {
  %0 = "tf.Const"() {value = dense<"foo"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
  %1 = "tf.Const"() {value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>

module {
  func.func @matmul2(%arg0: tensor<?x2xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<?x2xf32>) {
    %0 = stablehlo.constant dense<[-0.211145893, -0.708605706]> : tensor<2xf32>
    %1 = stablehlo.constant dense<[[-0.630731344, 0.54962182], [0.180364341, -0.764542698]]> : tensor<2x2xf32>

module {
  // CHECK-LABEL: func.func @main
  func.func @main(%arg0: tensor<1x1024xf32>) -> tensor<1x3xf32> {
    // CHECK: call @composite_dot_general_fn_1
    // CHECK-SAME: (tensor<1x1024xf32>, tensor<1024x3xf32>) -> tensor<1x3xf32>
    // CHECK-NOT: tf.XlaCallModule
    %0 = "tf.Const"() <{value = dense<0.5> : tensor<1024x3xf32>}> : () -> tensor<1024x3xf32>

func.func @QuantizeAssignVariableWithDequantAndEqualType(%arg0 : tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>) -> tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>> {
  %0 = "tfl.var_handle"() : () -> tensor<!tf_type.resource>
  %1 = "tfl.dequantize"(%arg0) : (tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>) -> tensor<1x2x1x3xf32>
  "tfl.assign_variable"(%0, %1) : (tensor<!tf_type.resource>, tensor<1x2x1x3xf32>) -> ()
  func.return %arg0 : tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>

func.func @dont_merge_quantization_followed_by_quantization(%arg0: tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32> {
  // CHECK: %[[QUANT_ARG_0:.*]] = stablehlo.uniform_quantize %[[ARG_0]]
  // CHECK: %[[DEQUANT:.*]] = stablehlo.uniform_dequantize %[[QUANT_ARG_0]]
  // CHECK: return %[[DEQUANT]]
  %quant_arg = stablehlo.uniform_quantize %arg0 : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0039207626791561354:-128>>

module attributes {tf_saved_model.semantics} {
  func.func @no_call_once(%arg0: tensor<i64> {tf_saved_model.index_path = ["x"]}) -> (tensor<i64> {tf_saved_model.index_path = ["r"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "output:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    func.return %arg0 : tensor<i64>
    // CHECK-LABEL: no_call_once
    // CHECK-NOT: "tfl.call_once"
  }

module {
func.func @degenerateCase(%arg0: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tfl.add"(%arg0, %arg0) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %0 : tensor<1xf32>
}
}

// CHECK:     func.func @degenerateCase(%arg0: tensor<1xf32>) -> tensor<1xf32> {

  %0:2 = "tfl.while"(%cst_0, %cst_1) ({
  ^bb0(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>):
    // CHECK: call @WhileOp_cond
    // CHECK-SAME: (tensor<*xi32>, tensor<*xf32>, tensor<i32>)
    %1 = func.call @WhileOp_cond(%arg0, %arg1, %cst) : (tensor<*xi32>, tensor<*xf32>, tensor<i32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>):
    // CHECK: call @WhileOp_body

// Converts a dag of HLOs representing floor_mod to tf.FloorMod.
// The pattern matched executes the following computation:
//
// rem = remainder(arg0, arg1)
// for i in 0 to len(arg1):
//    if ((rem[i] < 0) != (arg0[i] < 0) && arg0[i] != 0)
//       rem[i] += arg1[i]
// return rem
def : Pat<(MHLO_SelectOp
            (MHLO_AndOp
              (MHLO_CompareOp
                (MHLO_CompareOp:$rltz

        "Property<Boolean>"           | "boolean"        | "self.getProperty().getOrElse(false)"            | ".set(arg0)"       | []
        "Property<Long>"              | "long"           | "self.getProperty().getOrElse(0L)"               | ".set(arg0)"       | []