%7 = "tfl.reduce_all"(%arg, %axis_2) { keep_dims = true } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x2x1x3xi1>
  %8 = "tfl.reduce_all"(%arg, %axis_3) { keep_dims = false } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x2x1xi1>
  %9 = "tfl.reduce_all"(%arg, %axis_3) { keep_dims = true } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x2x1x1xi1>

    %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.

}

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:           %[[VAL_3:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<1x2x3x4xf32>}> : () -> tensor<1x2x3x4xf32>
// CHECK:           %[[VAL_4:.*]] = "tf.NotEqual"(%[[VAL_0]], %[[VAL_1]]) <{incompatible_shape_error = true}> : (tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xi1>

// CHECK: %[[EXPAND1:.+]] = "tfl.expand_dims"(%[[EXPAND0]], %[[EXPAND_DIM1]]) : (tensor<1x2x1x!quant.uniform<i8:f32, 2.000000e+00:3>>, tensor<1xi32>) -> tensor<1x2x1x1x!quant.uniform<i8:f32, 2.000000e+00:3>>
// CHECK: %[[BROADCAST:.+]] = "tfl.broadcast_to"(%[[EXPAND1]], %[[BROADCAST_DIM]]) : (tensor<1x2x1x1x!quant.uniform<i8:f32, 2.000000e+00:3>>, tensor<4xi32>) -> tensor<3x2x1x1x!quant.uniform<i8:f32, 2.000000e+00:3>>
// CHECK: return %[[BROADCAST]]

// -----

// 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>
}