}

func.func private @testIf3Then(tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>)
func.func private @testIf3Else(tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>)

// CHECK-LABEL: func @testIf3Result(%arg0: tensor<i1>, %arg1: tensor<*xf32>)
func.func @testIf3Result(tensor<i1>, tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>) {
^bb0(%arg0: tensor<i1>, %arg1: tensor<*xf32>):

// CHECK-SAME: (tensor<1x2x2x3xi8>, tensor<2x2x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>, tensor<f32>, tensor<i32>) -> tensor<*xi8>
// CHECK: %[[cast_1:.*]] = "tf.Cast"(%[[conv_quant]]) <{Truncate = false}> : (tensor<*xi8>) -> tensor<*xf32>

  func.return %11 : tensor<*xf32>
}

// CHECK: %[[maxpool_i8:.*]] = "tf.MaxPool"
// CHECK-SAME: (tensor<*xi8>) -> tensor<*xi8>
// CHECK: %[[reshape_i8:.*]] = "tf.Reshape"(%[[maxpool_i8]]
// CHECK-SAME: (tensor<*xi8>, tensor<2xi32>) -> tensor<*xi8>
// CHECK: %[[scast:.*]] = "quantfork.scast"(%[[reshape_i8]]
// CHECK: %[[matmul:.*]] = "tf.PartitionedCall"(%[[scast]]
// CHECK-SAME: f = @composite_matmul_fn_1

// CHECK: return %[[MATMUL]] : tensor<*xf32>

// CHECK-LABEL: func.func private @composite_dequantize_uniform(%arg0: tensor<*xi8>) -> tensor<*xf32>
// CHECK-DAG: %[[SCALE:.*]] = "tf.Const"() <{value = dense<0.0157480314> : tensor<f32>
// CHECK: %[[CASTED_W:.*]] = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<*xi8>) -> tensor<*xf32>
// CHECK: %[[DEQUANTIZED:.*]] = "tf.Mul"(%[[CASTED_W]], %[[SCALE]]) : (tensor<*xf32>, tensor<f32>) -> tensor<*xf32>

  // Argument has rank 2, but its dimensions are unknown.
  constexpr absl::string_view kArgumentWithUnknownDims = R"mlir(
    func.func @unknown_dims_arg(%arg: tensor<?x?xi8>) -> tensor<?x?xi8> {
      return %arg : tensor<?x?xi8>
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kArgumentWithUnknownDims);
  ASSERT_TRUE(module_op);

// operate on quantized values.
//
// Examples from storage to quantized type:
//   i8 -> !quant<"uniform[i8:f32]{1.0}">
//   tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
//   vector<4xi8> -> vector<4x!quant<"uniform[i8:f32]{1.0}">>
def Quantization_StorageCastOp : Quantization_Op<"scast", [Pure]> {
  let arguments = (ins quant_RealOrStorageValueType:$arg);

// test stablehlo roundtrip

// Identity function to make the exporter happy
func.func @main(%arg0: tensor<4xi8>) -> tensor<4xi8> {
  func.return %arg0 : tensor<4xi8>
}

//CHECK:func.func @main(%arg0: tensor<4xi8>) -> tensor<4xi8> attributes {tf.entry_function = {inputs = "arg0", outputs = "arg0"}} {
//CHECK: return %arg0 : tensor<4xi8>
//CHECK:}

func.func @logistic(%arg0: tensor<1x1x1x96xf32>) -> tensor<1x1x1x96xf32> {

// operate on quantized values.
//
// Examples from storage to quantized type:
//   i8 -> !quant<"uniform[i8:f32]{1.0}">
//   tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
//   vector<4xi8> -> vector<4x!quant<"uniform[i8:f32]{1.0}">>
def quantfork_StorageCastOp : quantfork_Op<"scast", [Pure]> {
  let arguments = (ins quant_RealOrStorageValueType:$arg);

}

TEST_F(ConvertTfQuantToMhloIntTest, UniformRequantizeToValidGraph) {
  constexpr absl::string_view kProgram = R"mlir(
func.func @main(%input: tensor<10xi8>) -> tensor<10xi8> {
  %input_scale = "tf.Const"() { value = dense<0.2235> : tensor<f32> } : () -> tensor<f32>
  %input_zp = "tf.Const"() { value = dense<-2> : tensor<i32> } : () -> tensor<i32>

// CHECK-LABEL: fused_conv2d
func.func @fused_conv2d(%input: tensor<1x300x300x40xi8>,
                        %filter: tensor<3x3x40x40xi8>,
                        %bias: tensor<40xf32>,
                        %act: tensor<0xi8>) -> tensor<1x300x300x40xi8> {

  // CHECK:       %[[v0:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>
  // CHECK-NEXT:  %[[v1:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>