%3 = "tfl.reduce_any"(%arg, %axis_0) { keep_dims = true } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x2x1x3xi1>
  %4 = "tfl.reduce_any"(%arg, %axis_1) { keep_dims = false } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x1x1x3xi1>
  %5 = "tfl.reduce_all"(%arg, %axis_1) { keep_dims = true } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x1x3xi1>

    %2 = stablehlo.constant dense<-128> : tensor<1x1x1x1xi8>  // Input zero point.
    %3 = stablehlo.constant dense<1> : tensor<3x3x4x4xi8>  // Quantized filter tensor.
    %4 = stablehlo.constant dense<3.000000e+03> : tensor<1x1x1x4xf32>
    %5 = stablehlo.constant dense<4.000000e+03> : tensor<1x1x1x1xf32>  // Output inverse scale.
    %6 = stablehlo.constant dense<127> : tensor<1x1x1x1xi8>  // Output zero point.

  %3 = "tfl.quantize"(%2) {qtype = tensor<1x1x1x3x!quant.uniform<i8:f32, 0.1>>, volatile} : (tensor<1x1x1x3xf32>) -> tensor<1x1x1x3x!quant.uniform<i8:f32, 0.1>>
  %4 = "tfl.dequantize"(%3) : (tensor<1x1x1x3x!quant.uniform<i8:f32, 0.1>>) -> tensor<1x1x1x3xf32>

}

TEST_F(AttrsAndConstraintsTest, HasRankOfReturnsTrueForMatchingRank) {
  constexpr absl::string_view kConstantOpWithRankFour =
      R"mlir(%0 = stablehlo.constant dense<0> : tensor<1x1x1x1xi8>)mlir";
  OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kConstantOpWithRankFour);
  ASSERT_TRUE(module_op);

  ASSERT_FALSE(module_op->getBodyRegion().empty());

// CHECK-DAG: %[[CONST_6:.*]] = "tf.Const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>
// CHECK-DAG: %[[CONST_7:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<1x1x1x3xi32>}> : () -> tensor<1x1x1x3xi32>
// CHECK-DAG-SAME{LITERAL}: value = dense<[[[[55040, -15104, -21376]]]]>
// CHECK-DAG: %[[CONST_8:.*]] = "tf.Const"() <{value = dense<[129, 166, 221]> : tensor<3xi32>}> : () -> tensor<3xi32>

  %1 = stablehlo.constant() {value = dense<5> : tensor<1x1x1x3xi32>} : () -> tensor<1x1x1x3x!quant.uniform<i32:f32:3, {2.000000e+00, 2.000000e+00, 2.000000e+00}>>

    // CHECK-DAG:  %[[ONE:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
    // CHECK:   %[[PRED:.*]] = "tf.Greater"(%[[FEATURES]], %[[ZERO]]) : (tensor<1x4x4x3xf32>, tensor<f32>) -> tensor<1x4x4x3xi1>
    // CHECK-NEXT:  %[[SCALED_FEATURES:.*]] = "tf.Mul"(%[[FEATURES]], %[[SCALE]]) : (tensor<1x4x4x3xf32>, tensor<f32>) -> tensor<1x4x4x3xf32>

// -----

// CHECK-LABEL: testSelectV2With4DBroadcasting
func.func @testSelectV2With4DBroadcasting(%cond : tensor<1x1x3x1xi1>, %arg0 : tensor<1x1x1x4xf32>, %arg1 : tensor<1x2x1x1xf32>) -> tensor<1x2x3x4xf32> {
  // CHECK: "tfl.select_v2"(%arg0, %arg1, %arg2)
  %0 = "tfl.select_v2"(%cond, %arg0, %arg1): (tensor<1x1x3x1xi1>, tensor<1x1x1x4xf32>, tensor<1x2x1x1xf32>) -> tensor<1x2x3x4xf32>
  func.return %0 : tensor<1x2x3x4xf32>
}

    // CHECK-NEXT: %[[CMP:.*]] = mhlo.compare GT, %[[INP]], %[[ZERO]], NOTYPE : (tensor<1x4x4x3xf32>, tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xi1>
    // CHECK-NEXT: %[[RES:.*]] = mhlo.select %[[CMP]], %[[INP]], %[[LEAKY]] : tensor<1x4x4x3xi1>, tensor<1x4x4x3xf32>
    // CHECK-NEXT: return %[[RES]] : tensor<1x4x4x3xf32>