}

TEST_F(ConvertTfQuantToMhloIntTest, UniformQuantizeDotToValidGraph) {
  constexpr absl::string_view kProgram = R"mlir(
func.func @main(%input: tensor<8x9xi8>, %filter: tensor<9x10xi8>) -> tensor<8x10xi32> {
  %input_scale = "tf.Const"() { value = dense<0.588> : tensor<f32> } : () -> tensor<f32>
  %input_zp = "tf.Const"() { value = dense<42> : tensor<i32> } : () -> tensor<i32>

}

// -----

func.func @testPadUnsupportedPaddings(tensor<*xf32>, tensor<6x3xi32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<*xf32>, %arg1: tensor<6x3xi32>):
  // expected-error @+1 {{'tfl.pad' op failed to verify that the first dim size of the padding argument must be at most 5}}
  %0 = "tfl.pad"(%arg0, %arg1) : (tensor<*xf32>, tensor<6x3xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>
}

// -----

}

// -----

// CHECK-LABEL: testSelectInvalidUnranked
func.func @testSelectInvalidUnranked(%arg0: tensor<6x7xi1>, %arg1: tensor<*xf16>, %arg2: tensor<*xf16>) -> tensor<*xf16> {
  // CHECK-NEXT: tf.Select
  %0 = "tf.Select"(%arg0, %arg1, %arg2) : (tensor<6x7xi1>, tensor<*xf16>, tensor<*xf16>) -> tensor<*xf16>
  func.return %0: tensor<*xf16>
}

// -----

  // CHECK-DAG: %[[LOSS_INP:.*]] = "tf.Mul"(%[[LOG_SOFTMAX]], %[[NEG_LABELS]]) : (tensor<2x3xf32>, tensor<2x3xf32>) -> tensor<2x3xf32>
  // CHECK-DAG: %[[SAFE_LOSS_INP:.*]] = "tf.SelectV2"(%[[IS_LABEL_ZERO]], %[[ZERO]], %[[LOSS_INP]]) : (tensor<2x3xi1>, tensor<f32>, tensor<2x3xf32>) -> tensor<2x3xf32>

func.func @DequantizeAndQuantize() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {
  %cst = "tfl.pseudo_qconst"() {qtype = tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>, value = dense<-1> : tensor<2x2xi8>} : () -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>
  %0 = "tfl.dequantize"(%cst) : (tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>) -> tensor<2x2xf32>