%4 = "tfl.dequantize"(%3) : (tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>) -> tensor<32x3x3x3xf32>

// set of acceptable options.
// LINT.IfChange
enum BuiltinOperator : int32 {
  ADD = 0,
  AVERAGE_POOL_2D = 1,
  CONCATENATION = 2,
  CONV_2D = 3,
  DEPTHWISE_CONV_2D = 4,
  DEPTH_TO_SPACE = 5,
  DEQUANTIZE = 6,
  EMBEDDING_LOOKUP = 7,
  FLOOR = 8,
  FULLY_CONNECTED = 9,
  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,

// set of acceptable options.
// LINT.IfChange
enum BuiltinOperator : int32 {
  ADD = 0,
  AVERAGE_POOL_2D = 1,
  CONCATENATION = 2,
  CONV_2D = 3,
  DEPTHWISE_CONV_2D = 4,
  DEPTH_TO_SPACE = 5,
  DEQUANTIZE = 6,
  EMBEDDING_LOOKUP = 7,
  FLOOR = 8,
  FULLY_CONNECTED = 9,
  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,

// CHECK: %[[PAD:.+]] = "tfl.pad"(%[[ARG_0]], %[[CONST_0]]) : (tensor<1x4x5x3x!quant.uniform<i8:f32, 2.000000e+00>>, tensor<4x2xi32>) -> tensor<1x6x7x3x!quant.uniform<i8:f32, 2.000000e+00>>

                                     Type result_type, Value input,
                                     Value filter, Value bias) const {
    // Compared to tfl.conv_2d, tfl.depthwise_conv_2d has an additional
    // 'depth_multiplier' attribute. However, tf.DepthwiseConv2dNative does not
    // have a corresponding 'depth_multiplier' attribute; the multiplier is the

      for (int k = 0; k < filter_shape[2]; ++k) {
        for (int l = 0; l < filter_shape[3]; ++l) {
          // [o, 0, 1, i] for `tfl.conv_2d` case`,
          // [i, 0, 1, o] for `tfl.depthwise_conv_2d` case.
          int old_idx = get_array_idx(filter_shape, i, j, k, l);
          int new_idx = is_depthwise
                            ? get_array_idx(new_filter_shape, k, i, j, l)

  %w = arith.constant dense<127.0> : tensor<32x3x3x3xf32>
  %b = arith.constant dense<0.0> : tensor<32x1xf32>
  // expected-error @+1 {{'tfl.depthwise_conv_2d' op operand #2 must be 1D tensor of any type values or none type, but got 'tensor<32x1xf32>'}}

      succeeded(applyPatternsAndFoldGreedily(test_func, std::move(patterns))));

  auto depthwise_conv_op =
      FindOperationOfType<TF::DepthwiseConv2dNativeOp>(test_func);
  EXPECT_THAT(depthwise_conv_op, NotNull());
  // The filter of the DepthwiseConv2dNativeOp is expected to be a constant.
  EXPECT_TRUE(isa<TF::ConstOp>(depthwise_conv_op.getFilter().getDefiningOp()));
}

TEST_F(ConstantFoldingTest, DepthwiseConvWeightNotFoldable) {

// CHECK-NEXT: %[[DEPTHWISE_CONV2D:.*]] = "tf.DepthwiseConv2dNative"(%arg0, %[[CONST]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1]}> {device = ""} : (tensor<*xf32>, tensor<2x3x3x1xf32>) -> tensor<?x?x?x3xf32>

// CHECK: func private @quantized_conv2d_with_relu6_fn
// CHECK: func private @quantized_depthwise_conv2d_with_bias_and_relu_float_output_fn
// CHECK-SAME: tf_quant.quantized_ops = ["DepthwiseConv2D", "BiasAdd", "Relu"]
// CHECK: func private @quantized_matmul_with_bias_fn
// CHECK: func private @quantized_matmul_with_bias_and_relu_fn
// CHECK: func private @quantized_matmul_with_bias_and_relu6_fn