module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  func.func @main() -> () {
    %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1>
    %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
    func.return
  }
})";

absl::StatusOr<XlaCompiler::CompilationResult> CompileMlirModule(
    const char* mlir_module_str,

    %9 = "tf.Identity"(%cst_2) {device = ""} : (tensor<3x4xi8>) -> tensor<3x4xi8>
    %10 = "tf.Cast"(%9) {Truncate = false, device = ""} : (tensor<3x4xi8>) -> tensor<3x4xi32>
    %11 = "tf.Einsum"(%8, %10) {device = "", equation = "ab,bc->ac"} : (tensor<2x3xi32>, tensor<3x4xi32>) -> tensor<2x4xi32>
    %12 = "tf.Cast"(%11) {Truncate = false, device = ""} : (tensor<2x4xi32>) -> tensor<2x4xf32>

// Ensure constants roundtrip exactly

func.func @bool() -> tensor<4xi1> {
  // CHECK-LABEL: @bool
  // CHECK: value = dense<[false, true, true, false]> : tensor<4xi1>
  %0 = "tfl.pseudo_const"() { value = dense<[false, true, true, false]> : tensor<4xi1> } : () -> tensor<4xi1>
  func.return %0 : tensor<4xi1>
}

func.func @complex64() -> tensor<4xcomplex<f32>> {
  // CHECK-LABEL: @complex64

// Ensure constants roundtrip exactly

func.func @bool() -> tensor<4xi1> {
  // CHECK-LABEL: @bool
  // CHECK: value = dense<[false, true, true, false]> : tensor<4xi1>
  %0 = "tfl.pseudo_const"() { value = dense<[false, true, true, false]> : tensor<4xi1> } : () -> tensor<4xi1>
  func.return %0 : tensor<4xi1>
}

func.func @complex64() -> tensor<4xcomplex<f32>> {
  // CHECK-LABEL: @complex64

// CHECK-LABEL: @quantize_constant
// CHECK-SAME: %[[ARG0:.*]]: tensor<1x3xf32>
func.func @quantize_constant(%arg0: tensor<1x3xf32>) -> tensor<1x2xf32> {
  // CHECK-DAG: %[[QCST:.*]] = stablehlo.constant() <{value = dense<-78> : tensor<3x2xi8>}> : () -> tensor<3x2x!quant.uniform<i8<-127:127>:f32, 5.000000e-03>>
  // CHECK-DAG: %[[Q1:.*]] = stablehlo.uniform_quantize %[[ARG0]]
  // CHECK-NOT: "quantfork.qcast"
  // CHECK: %[[DOT:.*]] = stablehlo.dot %[[Q1]], %[[QCST]]

}

func.func @concat_v2_with_bool_type(%arg0: tensor<?x1xi1>, %arg1: tensor<?x1xi1>) -> tensor<?x2xi1> {
  %0 = "tf.Const"() { value = dense<-1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.ConcatV2"(%arg0, %arg1, %0) : (tensor<?x1xi1>, tensor<?x1xi1>, tensor<i32>) -> tensor<?x2xi1>
  func.return %1 : tensor<?x2xi1>

// CHECK-LABEL: concat_v2_with_bool_type

  %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

  %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // CHECK: %[[input:.*]] = "tf.ConcatV2"(%arg0, %arg1, %[[VAL:.*]]) : (tensor<3x3xi8>, tensor<3x3xi8>, tensor<i64>) -> tensor<6x3xi8>
  // CHECK: %[[input_qint:.*]] = "tf.Cast"(%[[input]]) <{Truncate = false}> : (tensor<6x3xi8>) -> tensor<6x3x!tf_type.qint8>
  // CHECK: %[[output:.*]] = "tf.UniformDequantize"(%[[input_qint]]

func.func @select(
    %arg0: tensor<1x3xi1>,
    %arg1: tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>,
    %arg2: tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>
  ) -> tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>> {
  %0 = "stablehlo.select"(%arg0, %arg1, %arg2) : (
    tensor<1x3xi1>,
    tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>,

// CHECK: %[[UNIFORM_QUANTIZE:.+]] = stablehlo.uniform_quantize %[[ARG_0]] : (tensor<3x4x2xf32>) -> tensor<3x4x2x!quant.uniform<i8:f32, {{.*}}>>
// CHECK: %[[CALL:.+]] = call @quantized_gather_fn(%[[UNIFORM_QUANTIZE]], %[[CONST]]) {_quantization_method = "static_range_ptq { }"} : (tensor<3x4x2x!quant.uniform<i8:f32, {{.*}}>>, tensor<2x3x2xi32>) -> tensor<2x3x2x2x!quant.uniform<i8:f32, {{.*}}>>