}

TEST_F(ImageGradTest, TestBilinear) {
  TestResizedShape(RESIZE_BILINEAR);
  TestResize<float, float, float>(RESIZE_BILINEAR);
  // Note that Y_T is always float for this op. We choose
  // double for the jacobian to capture the higher precision
  // between X_T and Y_T.
  TestResize<double, float, double>(RESIZE_BILINEAR);
}

TEST_F(ImageGradTest, TestBicubic) {
  TestResizedShape(RESIZE_BICUBIC);

  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,
  RELU6 = 21,
  RESHAPE = 22,
  RESIZE_BILINEAR = 23,
  RNN = 24,
  SOFTMAX = 25,
  SPACE_TO_DEPTH = 26,
  SVDF = 27,
  TANH = 28,
  CONCAT_EMBEDDINGS = 29,
  SKIP_GRAM = 30,
  CALL = 31,
  CUSTOM = 32,
  EMBEDDING_LOOKUP_SPARSE = 33,

// CHECK-DAG:           %[[VAL_3:.*]] = arith.constant dense<32> : tensor<2xi32>
// CHECK-DAG:           %[[VAL_4:.*]] = "tfl.resize_bilinear"(%[[VAL_2]], %[[VAL_3]]) <{align_corners = false, half_pixel_centers = true}> : (tensor<1x16x16x64xf32>, tensor<2xi32>) -> tensor<1x32x32x64xf32>
// CHECK-DAG:           %[[VAL_5:.*]] = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>

  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,
  RELU6 = 21,
  RESHAPE = 22,
  RESIZE_BILINEAR = 23,
  RNN = 24,
  SOFTMAX = 25,
  SPACE_TO_DEPTH = 26,
  SVDF = 27,
  TANH = 28,
  CONCAT_EMBEDDINGS = 29,
  SKIP_GRAM = 30,
  CALL = 31,
  CUSTOM = 32,
  EMBEDDING_LOOKUP_SPARSE = 33,

  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,
  RELU6 = 21,
  RESHAPE = 22,
  RESIZE_BILINEAR = 23,
  RNN = 24,
  SOFTMAX = 25,
  SPACE_TO_DEPTH = 26,
  SVDF = 27,
  TANH = 28,
  CONCAT_EMBEDDINGS = 29,
  SKIP_GRAM = 30,
  CALL = 31,
  CUSTOM = 32,
  EMBEDDING_LOOKUP_SPARSE = 33,

                      llvm::cl::Optional, llvm::cl::init(false));

// NOLINTNEXTLINE
opt<bool> skip_resize(
    "skip-resize",
    llvm::cl::desc(
        "Skip converting tf.ResizeBilinear and tf.ResizeNearestNeighbor ops."),
    llvm::cl::Optional, llvm::cl::init(true));

// NOLINTNEXTLINE
opt<bool> smuggle_disallowed_ops(
    "smuggle-disallowed-ops",

  // b/135640736: MatrixInverse performance issues.
  // b/111271662: MatrixSolve performance issues.
  // https://github.com/tensorflow/tensorflow/pull/31012:
  //    ResizeNearestNeighbor, ResizeBilinear, and ResizeBilinearGrad sometimes
  //    create convolutions too large for CuDNN to handle.
  return node.type_string() == "SelfAdjointEigV2" ||
         node.type_string() == "Svd" || node.type_string() == "Qr" ||