text: #include <gtest/gtest.h>
  
  #include <torch/torch.h>
  
  #include <test/cpp/api/support.h>
  
  #include <random>
  #include <torch/nn/options/activation.h>
  #include <torch/nn/functional/activation.h>
  #include <torch/expanding_array.h>
  #include <limits>
  
  using namespace torch::nn;
  using namespace torch::test;
  
  class TestModel : public torch::nn::Module {
   public:
    TestModel()
        : l1(register_module("l1", Linear(10, 3))),
          l2(register_module("l2", Linear(3, 5))),
          l3(register_module("l3", Linear(5, 100))) {}
  
    Linear l1, l2, l3;
  };
  
  class NestedModel : public torch::nn::Module {
   public:
    NestedModel()
        : param_(register_parameter("param", torch::empty({3, 2, 21}))),
          l1(register_module("l1", Linear(5, 20))),
          t(register_module("test", std::make_shared<TestModel>())) {}
  
    torch::Tensor param_;
    Linear l1;
    std::shared_ptr<TestModel> t;
  };
  
  struct ModulesTest : torch::test::SeedingFixture {};
  
  TEST_F(ModulesTest, Conv1d) {
    Conv1d model(Conv1dOptions(3, 2, 3).stride(1).bias(false));
    model->weight.set_data(torch::arange(18, torch::dtype(torch::kFloat)).reshape({2, 3, 3}));
    auto x = torch::arange(30, torch::dtype(torch::kFloat).requires_grad(true)).reshape({2, 3, 5});
    auto y = model(x);
    auto expected = torch::tensor({{{ 312.,  348.,  384.},
                                    { 798.,  915., 1032.}},
  
                                   {{ 852.,  888.,  924.},
                                    {2553., 2670., 2787.}}}, torch::kFloat);
    ASSERT_TRUE(torch::allclose(y, expected));
  
    torch::Tensor s = y.sum();
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3);
  }
  
  TEST_F(ModulesTest, Conv1dSameStrided) {
    auto options = Conv1dOptions(3, 2, 3);
    options.stride(1).padding(torch::kSame);
    Conv1d model_valid(options);
    ASSERT_THROWS_WITH(
      [&]{ Conv1d model_invalid(options.stride(2)); }(),
      "padding='same' is not supported for strided convolutions");
  }
  
  TEST_F(ModulesTest, Conv2dEven) {
    Conv2d model(Conv2dOptions(3, 2, 3).stride(1).bias(false));
    model->weight.set_data(torch::arange(54, torch::dtype(torch::kFloat)).reshape({2, 3, 3, 3}));
    auto x = torch::arange(75, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 5});
    auto y = model(x);
    auto expected = torch::tensor({{{{15219., 15570., 15921.},
                                     {16974., 17325., 17676.},
                                     {18729., 19080., 19431.}},
  
                                    {{37818., 38898., 39978.},
                                     {43218., 44298., 45378.},
                                     {48618., 49698., 50778.}}}}, torch::kFloat);
    ASSERT_TRUE(torch::allclose(y, expected));
  
    torch::Tensor s = y.sum();
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 3);
  }
  
  TEST_F(ModulesTest, Conv2dUneven) {
    Conv2d model(Conv2dOptions(3, 2, {3, 2}).stride({1, 1}).bias(false));
    model->weight.set_data(torch::arange(36, torch::dtype(torch::kFloat)).reshape({2, 3, 3, 2}));
    auto x = torch::arange(60, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 4});
    auto y = model(x);
    auto expected = torch::tensor({{{{ 5289.,  5442.,  5595.},
                                     { 5901.,  6054.,  6207.},
                                     { 6513.,  6666.,  6819.}},
  
                                    {{13227., 13704., 14181.},
                                     {15135., 15612., 16089.},
                                     {17043., 17520., 17997.}}}}, torch::kFloat);
    ASSERT_TRUE(torch::allclose(y, expected));
  
    torch::Tensor s = y.sum();
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 2);
  }
  
  TEST_F(ModulesTest, Conv2dSameStrided) {
    auto options = Conv2dOptions(3, 2, {3, 4});
    options.stride(1).padding(torch::kSame);
    Conv2d model_valid(options);
    ASSERT_THROWS_WITH(
      [&]{ Conv2d model_invalid(options.stride(2)); }(),
      "padding='same' is not supported for strided convolutions");
    ASSERT_THROWS_WITH(
      [&]{ Conv2d model_invalid(options.stride({1, 2})); }(),
      "padding='same' is not supported for strided convolutions");
  }
  
  TEST_F(ModulesTest, Conv3d) {
    Conv3d model(Conv3dOptions(3, 2, 3).stride(1).bias(false));
    model->weight.set_data(torch::arange(162, torch::dtype(torch::kFloat)).reshape({2, 3, 3, 3, 3}));
    auto x = torch::arange(375, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 5, 5});
    auto y = model(x);
    auto expected = torch::tensor({{{{{ 700704.,  703944.,  707184.},
                                      { 716904.,  720144.,  723384.},
                                      { 733104.,  736344.,  739584.}},
  
                                     {{ 781704.,  784944.,  788184.},
                                      { 797904.,  801144.,  804384.},
                                      { 814104.,  817344.,  820584.}},
  
                                     {{ 862704.,  865944.,  869184.},
                                      { 878904.,  882144.,  885384.},
                                      { 895104.,  898344.,  901584.}}},
  
                                    {{{1724220., 1734021., 1743822.},
                                      {1773225., 1783026., 1792827.},
                                      {1822230., 1832031., 1841832.}},
  
                                     {{1969245., 1979046., 1988847.},
                                      {2018250., 2028051., 2037852.},
                                      {2067255., 2077056., 2086857.}},
  
                                     {{2214270., 2224071., 2233872.},
                                      {2263275., 2273076., 2282877.},
                                      {2312280., 2322081., 2331882.}}}}}, torch::kFloat);
    ASSERT_TRUE(torch::allclose(y, expected));
  
    torch::Tensor s = y.sum();
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_TRUE(model->weight.grad().numel() == 3 * 2 * 3 * 3 * 3);
  }
  
  TEST_F(ModulesTest, Conv3dSameStrided) {
    auto options = Conv3dOptions(3, 2, {3, 4, 5});
    options.stride(1).padding(torch::kSame);
    Conv3d model_valid(options);
    ASSERT_THROWS_WITH(
      [&]{ Conv3d model_invalid(options.stride(2)); }(),
      "padding='same' is not supported for strided convolutions");
    ASSERT_THROWS_WITH(
      [&]{ Conv3d model_invalid(options.stride({1, 2, 1})); }(),
      "padding='same' is not supported for strided convolutions");
  }
  
  TEST_F(ModulesTest, ConvTranspose1d) {
    ConvTranspose1d model(ConvTranspose1dOptions(3, 2, 3).stride(1).bias(false));
    model->weight.set_data(torch::arange(18.).view({2, 3, 3}));
    auto x = torch::arange(20.).reshape({2, 2, 5});
    auto y = model(x);
    auto expected = torch::tensor({{{  45.,  104.,  179.,  212.,  245.,  188.,  107.},
                                    {  60.,  140.,  242.,  293.,  344.,  260.,  146.},
                                    {  75.,  176.,  305.,  374.,  443.,  332.,  185.}},
                                   {{ 135.,  304.,  509.,  542.,  575.,  428.,  237.},
                                    { 210.,  460.,  752.,  803.,  854.,  620.,  336.},
                                    { 285.,  616.,  995., 1064., 1133.,  812.,  435.}}});
    ASSERT_TRUE(torch::allclose(y, expected));
  
    torch::Tensor s = y.sum();
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3);
  }
  
  TEST_F(ModulesTest, ConvTranspose2dEven) {
    ConvTranspose2d model(ConvTranspose2dOptions(3, 2, 3).stride(1).bias(false));
    model->weight.set_data(torch::arange(54.).view({2, 3, 3, 3}));
    auto x = torch::arange(50.).view({1, 2, 5, 5});
    auto y = model(x);
    auto expected = torch::tensor({{{{  675.,  1402.,  2183.,  2270.,  2357.,  1634.,   849.},
                                     { 1560.,  3240.,  5044.,  5236.,  5428.,  3760.,  1952.},
                                     { 2685.,  5574.,  8673.,  8988.,  9303.,  6438.,  3339.},
                                     { 3180.,  6594., 10248., 10563., 10878.,  7518.,  3894.},
                                     { 3675.,  7614., 11823., 12138., 12453.,  8598.,  4449.},
                                     { 2820.,  5832.,  9040.,  9268.,  9496.,  6544.,  3380.},
                                     { 1605.,  3314.,  5129.,  5252.,  5375.,  3698.,  1907.}},
                                    {{  900.,  1870.,  2912.,  3053.,  3194.,  2210.,  1146.},
                                     { 2100.,  4356.,  6772.,  7072.,  7372.,  5092.,  2636.},
                                     { 3630.,  7518., 11670., 12147., 12624.,  8706.,  4500.},
                                     { 4395.,  9078., 14055., 14532., 15009., 10326.,  5325.},
                                     { 5160., 10638., 16440., 16917., 17394., 11946.,  6150.},
                                     { 3900.,  8028., 12388., 12724., 13060.,  8956.,  4604.},
                                     { 2190.,  4502.,  6938.,  7115.,  7292.,  4994.,  2564.}},
                                    {{ 1125.,  2338.,  3641.,  3836.,  4031.,  2786.,  1443.},
                                     { 2640.,  5472.,  8500.,  8908.,  9316.,  6424.,  3320.},
                                     { 4575.,  9462., 14667., 15306., 15945., 10974.,  5661.},
                                     { 5610., 11562., 17862., 18501., 19140., 13134.,  6756.},
                                     { 6645., 13662., 21057., 21696., 22335., 15294.,  7851.},
                                     { 4980., 10224., 15736., 16180., 16624., 11368.,  5828.},
                                     { 2775.,  5690.,  8747.,  8978.,  9209.,  6290.,  3221.}}}});
    ASSERT_TRUE(torch::allclose(y, expected));
  
    torch::Tensor s = y.sum();
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 3);
  }
  
  TEST_F(ModulesTest, ConvTranspose2dUneven) {
    ConvTranspose2d model(ConvTranspose2dOptions(3, 2, {3, 2}).stride({1, 1}).bias(false));
    model->weight.set_data(torch::arange(36.).view({2, 3, 3, 2}));
    auto x = torch::arange(40.).view({1, 2, 5, 4});
    auto y = model(x);
    auto expected = torch::tensor({{{{ 360.,  758.,  796.,  834.,  440.},
                                     { 832., 1752., 1836., 1920., 1012.},
                                     {1432., 3014., 3152., 3290., 1732.},
                                     {1696., 3566., 3704., 3842., 2020.},
                                     {1960., 4118., 4256., 4394., 2308.},
                                     {1504., 3152., 3252., 3352., 1756.},
                                     { 856., 1790., 1844., 1898.,  992.}},
                                    {{ 480., 1010., 1072., 1134.,  596.},
                                     {1120., 2352., 2484., 2616., 1372.},
                                     {1936., 4058., 4268., 4478., 2344.},
                                     {2344., 4898., 5108., 5318., 2776.},
                                     {2752., 5738., 5948., 6158., 3208.},
                                     {2080., 4328., 4476., 4624., 2404.},
                                     {1168., 2426., 2504., 2582., 1340.}},
                                    {{ 600., 1262., 1348., 1434.,  752.},
                                     {1408., 2952., 3132., 3312., 1732.},
                                     {2440., 5102., 5384., 5666., 2956.},
                                     {2992., 6230., 6512., 6794., 3532.},
                                     {3544., 7358., 7640., 7922., 4108.},
                                     {2656., 5504., 5700., 5896., 3052.},
                                     {1480., 3062., 3164., 3266., 1688.}}}});
    ASSERT_TRUE(torch::allclose(y, expected));
  
    torch::Tensor s = y.sum();
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 2);
  }
  
  TEST_F(ModulesTest, ConvTranspose3d) {
    ConvTranspose3d model(ConvTranspose3dOptions(2, 2, 2).stride(1).bias(false));
    model->weight.set_data(torch::arange(32.).reshape({2, 2, 2, 2, 2}));
    auto x = torch::arange(16.).reshape({1, 2, 2, 2, 2});
    auto y = model(x);
    auto expected = torch::tensor({{{{{ 128.,  280.,  154.},
                                      { 304.,  664.,  364.},
                                      { 184.,  400.,  218.}},
                                     {{ 352.,  768.,  420.},
                                      { 832., 1808.,  984.},
                                      { 496., 1072.,  580.}},
                                     {{ 256.,  552.,  298.},
                                      { 592., 1272.,  684.},
                                      { 344.,  736.,  394.}}},
                                    {{{ 192.,  424.,  234.},
                                      { 464., 1016.,  556.},
                                      { 280.,  608.,  330.}},
                                     {{ 544., 1184.,  644.},
                                      {1280., 2768., 1496.},
                                      { 752., 1616.,  868.}},
                                     {{ 384.,  824.,  442.},
                                      { 880., 1880., 1004.},
                                      { 504., 1072.,  570.}}}}});
    ASSERT_TRUE(torch::allclose(y, expected));
  
    torch::Tensor s = y.sum();
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_TRUE(model->weight.grad().numel() == 2 * 2 * 2 * 2 * 2);
  }
  
  TEST_F(ModulesTest, MaxPool1d) {
    MaxPool1d model(MaxPool1dOptions(3).stride(2));
    auto x = torch::ones({1, 1, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1 ,2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 2}));
  }
  
  TEST_F(ModulesTest, MaxPool1dReturnIndices) {
    MaxPool1d model(MaxPool1dOptions(3).stride(2));
    auto x = torch::ones({1, 1, 5}, torch::requires_grad());
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
  
    ASSERT_EQ(y.dim(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1 ,2})));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 2}));
  
    ASSERT_TRUE(torch::allclose(indices, torch::tensor({{{0, 2}}}, torch::kLong)));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({1, 1, 2}));
  }
  
  TEST_F(ModulesTest, MaxPool2dEven) {
    MaxPool2d model(MaxPool2dOptions(3).stride(2));
    auto x = torch::ones({2, 5, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2 ,2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
  }
  
  TEST_F(ModulesTest, MaxPool2dUneven) {
    MaxPool2d model(MaxPool2dOptions({3, 2}).stride({2, 2}));
    auto x = torch::ones({2, 5, 4}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
  }
  
  TEST_F(ModulesTest, MaxPool2dReturnIndices) {
    MaxPool2d model(MaxPool2dOptions(3).stride(2));
    auto x = torch::ones({2, 5, 5}, torch::requires_grad());
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
  
    ASSERT_EQ(y.dim(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2 ,2})));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
    ASSERT_TRUE(torch::allclose(
      indices,
      torch::tensor({{{ 0,  2},
                      {10, 12}},
                     {{ 0,  2},
                      {10, 12}}}, torch::kLong)));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 2, 2}));
  }
  
  TEST_F(ModulesTest, MaxPool3d) {
    MaxPool3d model(MaxPool3dOptions(3).stride(2));
    auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
  }
  
  TEST_F(ModulesTest, MaxPool3dReturnIndices) {
    MaxPool3d model(MaxPool3dOptions(3).stride(2));
    auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
  
    ASSERT_EQ(y.dim(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
  
    ASSERT_TRUE(torch::allclose(
      indices,
      torch::tensor({{{{ 0,  2},
                       {10, 12}},
                      {{50, 52},
                       {60, 62}}},
                     {{{ 0,  2},
                       {10, 12}},
                      {{50, 52},
                       {60, 62}}}}, torch::kLong)));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
  }
  
  TEST_F(ModulesTest, AvgPool1d) {
    AvgPool1d model(AvgPool1dOptions(3).stride(2));
    auto x = torch::ones({1, 1, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1, 2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 2}));
  }
  
  TEST_F(ModulesTest, AvgPool2dEven) {
    AvgPool2d model(AvgPool2dOptions(3).stride(2));
    auto x = torch::ones({2, 5, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
  }
  
  TEST_F(ModulesTest, AvgPool2dUneven) {
    AvgPool2d model(AvgPool2dOptions({3, 2}).stride({2, 2}));
    auto x = torch::ones({2, 5, 4}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
  }
  
  TEST_F(ModulesTest, AvgPool3d) {
    AvgPool3d model(AvgPool3dOptions(3).stride(2));
    auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
  }
  
  TEST_F(ModulesTest, FractionalMaxPool2d) {
    FractionalMaxPool2d model(FractionalMaxPool2dOptions(3).output_size(2));
    auto x = torch::ones({2, 5, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
  }
  
  TEST_F(ModulesTest, FractionalMaxPool2dReturnIndices) {
    FractionalMaxPool2d model(FractionalMaxPool2dOptions(3).output_size(2));
    auto x = torch::ones({2, 5, 5}, torch::requires_grad());
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
  
    ASSERT_EQ(y.dim(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
    ASSERT_TRUE(torch::allclose(
      indices,
      torch::tensor({{{ 0,  2},
                      {10, 12}},
                     {{ 0,  2},
                      {10, 12}}})));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 2, 2}));
  }
  
  TEST_F(ModulesTest, FractionalMaxPool3d) {
    FractionalMaxPool3d model(FractionalMaxPool3dOptions(3).output_size(2));
    auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
  }
  
  TEST_F(ModulesTest, FractionalMaxPool3dReturnIndices) {
    FractionalMaxPool3d model(FractionalMaxPool3dOptions(3).output_size(2));
    auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
  
    ASSERT_EQ(y.dim(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
  
    ASSERT_TRUE(torch::allclose(
      indices,
      torch::tensor({{{{ 0,  2},
                       {10, 12}},
                      {{50, 52},
                       {60, 62}}},
                     {{{ 0,  2},
                       {10, 12}},
                      {{50, 52},
                       {60, 62}}}})));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
  }
  
  TEST_F(ModulesTest, LPPool1d) {
    int norm_type = 2;
    int stride = 2;
    int kernel_size = 3;
  
    LPPool1d model(LPPool1dOptions(norm_type, kernel_size).stride(stride));
    auto x = torch::ones({1, 1, 5});
    auto y = model(x);
    auto expected = (torch::pow(torch::tensor({{{1, 1}}}, torch::kFloat), norm_type) * kernel_size).pow(1. / norm_type);
  
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, expected));
    ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 2}));
  }
  
  TEST_F(ModulesTest, LPPool2d) {
    int norm_type = 2;
    int stride = 2;
    std::vector<int64_t> kernel_size({2, 3});
  
    LPPool2d model(LPPool2dOptions(norm_type, kernel_size).stride(stride));
    auto x = torch::ones({1, 2, 5});
    auto y = model(x);
    auto expected = (torch::pow(torch::tensor({{{1, 1}}}, torch::kFloat), norm_type) * (kernel_size[0] * kernel_size[1])).pow(1. / norm_type);
  
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, expected));
    ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 2}));
  }
  
  TEST_F(ModulesTest, Identity) {
    Identity identity;
    auto input = torch::tensor({{1, 3, 4}, {2, 3, 4}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto output = identity->forward(input);
    auto expected = torch::tensor({{1, 3, 4}, {2, 3, 4}}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(torch::equal(output, expected));
    ASSERT_TRUE(torch::equal(input.grad(), torch::ones_like(input)));
  }
  
  TEST_F(ModulesTest, Flatten) {
    Flatten flatten;
    auto input = torch::tensor({{1, 3, 4}, {2, 5, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto output = flatten->forward(input);
    auto expected = torch::tensor({{1, 3, 4}, {2, 5, 6}}, torch::kFloat);
    auto s = output.sum();
  
    s.backward();
    ASSERT_TRUE(torch::equal(output, expected));
    ASSERT_TRUE(torch::equal(input.grad(), torch::ones_like(input)));
  
    // Testing with optional arguments start_dim and end_dim
    Flatten flatten_optional_dims(FlattenOptions().start_dim(2).end_dim(3));
    input = torch::tensor({
      {{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}},
      {{{9, 10}, {11, 12}}, {{13, 14}, {15, 16}}}
     }, torch::dtype(torch::kFloat).requires_grad(true)); // Tensor with sizes (2, 2, 2, 2)
  
    output = flatten_optional_dims->forward(input);
    expected = torch::tensor({
      {{1, 2, 3, 4}, {5, 6, 7, 8}},
      {{9, 10, 11, 12}, {13, 14, 15, 16}}
     }, torch::kFloat); // Tensor with sizes (2, 2, 4)
  
    s = output.sum();
    s.backward();
    ASSERT_TRUE(torch::equal(output, expected));
    ASSERT_TRUE(torch::equal(input.grad(), torch::ones_like(input)));
  }
  
  TEST_F(ModulesTest, Unflatten) {
    // Non-named tensor
    Unflatten unflatten(UnflattenOptions(0, {2, 2}));
    auto output = unflatten->forward(torch::tensor({1, 2, 3, 4}));
    auto expected = torch::tensor({{1, 2}, {3, 4}});
    ASSERT_TRUE(torch::equal(output, expected));
  
    // Named tensor
    auto make_dimnames = [](std::vector<std::string> names) {
      std::vector<torch::Dimname> dimnames;
      // NOLINTNEXTLINE(performance-for-range-copy)
      for (auto name : names) {
        // NOLINTNEXTLINE(performance-inefficient-vector-operation)
        dimnames.push_back(
            torch::Dimname::fromSymbol(torch::Symbol::dimname(name)));
      }
      return dimnames;
    };
  
    unflatten = Unflatten(UnflattenOptions(
        "B",
        {std::pair<std::string, int64_t>{"B1", 2},
         std::pair<std::string, int64_t>{"B2", 2}}));
    output = unflatten->forward(
        torch::tensor({{1, 2, 3, 4}}).refine_names(make_dimnames({"A", "B"})));
    expected = torch::tensor({{{1, 2}, {3, 4}}})
                   .refine_names(make_dimnames({"A", "B1", "B2"}));
    ASSERT_TRUE(torch::equal(output, expected));
  }
  
  TEST_F(ModulesTest, AdaptiveMaxPool1d) {
    AdaptiveMaxPool1d model(3);
    auto x = torch::tensor({{{1, 2, 3, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({{{2, 4, 5}}}, torch::kFloat)));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3}));
  }
  
  TEST_F(ModulesTest, AdaptiveMaxPool1dReturnIndices) {
    AdaptiveMaxPool1d model(3);
    auto x = torch::tensor({{{1, 2, 3, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
  
    ASSERT_EQ(y.dim(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({{{2, 4, 5}}}, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3}));
    ASSERT_TRUE(torch::allclose(indices, torch::tensor({{{1, 3, 4}}}, torch::kLong)));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({1, 1, 3}));
  }
  
  TEST_F(ModulesTest, AdaptiveMaxPool2dEven) {
    AdaptiveMaxPool2d model(3);
    auto x = torch::arange(0., 50);
    x.resize_({2, 5, 5}).set_requires_grad(true);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{6, 8, 9},
       {16, 18, 19},
       {21, 23, 24}},
      {{31, 33, 34},
       {41, 43, 44},
       {46, 48, 49}},
    }, torch::kFloat)));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3}));
  }
  
  TEST_F(ModulesTest, AdaptiveMaxPool2dUneven) {
    AdaptiveMaxPool2d model(AdaptiveMaxPool2dOptions({3, 2}));
    auto x = torch::arange(0., 40);
    x.resize_({2, 5, 4}).set_requires_grad(true);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{5, 7},
       {13, 15},
       {17, 19}},
      {{25, 27},
       {33, 35},
       {37, 39}},
    }, torch::kFloat)));
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 2}));
  }
  
  TEST_F(ModulesTest, AdaptiveMaxPool2dReturnIndicesEven) {
    AdaptiveMaxPool2d model(3);
    auto x = torch::arange(0., 50);
    x.resize_({2, 5, 5}).set_requires_grad(true);
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{6, 8, 9},
       {16, 18, 19},
       {21, 23, 24}},
      {{31, 33, 34},
       {41, 43, 44},
       {46, 48, 49}},
    }, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3}));
  
    ASSERT_EQ(indices.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(indices, torch::tensor({
      {{6, 8, 9},
       {16, 18, 19},
       {21, 23, 24}},
      {{6, 8, 9},
       {16, 18, 19},
       {21, 23, 24}},
    }, torch::kLong)));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 3, 3}));
  }
  
  TEST_F(ModulesTest, AdaptiveMaxPool2dReturnIndicesUneven) {
    AdaptiveMaxPool2d model(AdaptiveMaxPool2dOptions({3, 2}));
    auto x = torch::arange(0., 40);
    x.resize_({2, 5, 4}).set_requires_grad(true);
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{5, 7},
       {13, 15},
       {17, 19}},
      {{25, 27},
       {33, 35},
       {37, 39}},
    }, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 2}));
  
    ASSERT_EQ(indices.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(indices, torch::tensor({
      {{5, 7},
       {13, 15},
       {17, 19}},
      {{5, 7},
       {13, 15},
       {17, 19}},
    }, torch::kLong)));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 3, 2}));
  }
  
  TEST_F(ModulesTest, AdaptiveMaxPool3d) {
    AdaptiveMaxPool3d model(3);
    auto x = torch::arange(0., 64);
    x.resize_({1, 4, 4, 4}).set_requires_grad(true);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{21, 22, 23},
       {25, 26, 27},
       {29, 30, 31}},
      {{37, 38, 39},
       {41, 42, 43},
       {45, 46, 47}},
      {{53, 54, 55},
       {57, 58, 59},
       {61, 62, 63}},
    }, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 3, 3, 3}));
  }
  
  TEST_F(ModulesTest, AdaptiveMaxPool3dReturnIndices) {
    AdaptiveMaxPool3d model(3);
    auto x = torch::arange(0., 64);
    x.resize_({1, 4, 4, 4}).set_requires_grad(true);
    torch::Tensor y, indices;
    std::tie(y, indices) = model->forward_with_indices(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{21, 22, 23},
       {25, 26, 27},
       {29, 30, 31}},
      {{37, 38, 39},
       {41, 42, 43},
       {45, 46, 47}},
      {{53, 54, 55},
       {57, 58, 59},
       {61, 62, 63}},
    }, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 3, 3, 3}));
  
    ASSERT_EQ(indices.ndimension(), 4);
    ASSERT_TRUE(torch::allclose(indices, torch::tensor({
      {{21, 22, 23},
       {25, 26, 27},
       {29, 30, 31}},
      {{37, 38, 39},
       {41, 42, 43},
       {45, 46, 47}},
      {{53, 54, 55},
       {57, 58, 59},
       {61, 62, 63}},
    }, torch::kLong)));
    ASSERT_EQ(indices.sizes(), std::vector<int64_t>({1, 3, 3, 3}));
  }
  
  TEST_F(ModulesTest, AdaptiveAvgPool1d) {
    AdaptiveAvgPool1d model(3);
    auto x = torch::tensor({{{1, 2, 3, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({{{1.5, 3.0, 4.5}}}, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3}));
  }
  
  TEST_F(ModulesTest, AdaptiveAvgPool2dEven) {
    AdaptiveAvgPool2d model(3);
    auto x = torch::arange(0., 50);
    x.resize_({2, 5, 5}).set_requires_grad(true);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{ 3.0,  4.5,  6.0},
       {10.5, 12.0, 13.5},
       {18.0, 19.5, 21.0}},
      {{28.0, 29.5, 31.0},
       {35.5, 37.0, 38.5},
       {43.0, 44.5, 46.0}},
    }, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3}));
  }
  
  TEST_F(ModulesTest, AdaptiveAvgPool2dUneven) {
    AdaptiveAvgPool2d model(AdaptiveAvgPool2dOptions({3, 2}));
    auto x = torch::arange(0., 40);
    x.resize_({2, 5, 4}).set_requires_grad(true);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{2.5, 4.5},
       {8.5, 10.5},
       {14.5, 16.5}},
      {{22.5, 24.5},
       {28.5, 30.5},
       {34.5, 36.5}},
    }, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 2}));
  }
  
  TEST_F(ModulesTest, AdaptiveAvgPool3d) {
    AdaptiveAvgPool3d model(3);
    auto x = torch::arange(0., 64);
    x.resize_({1, 4, 4, 4}).set_requires_grad(true);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::tensor({
      {{10.5, 11.5, 12.5},
       {14.5, 15.5, 16.5},
       {18.5, 19.5, 20.5}},
      {{26.5, 27.5, 28.5},
       {30.5, 31.5, 32.5},
       {34.5, 35.5, 36.5}},
      {{42.5, 43.5, 44.5},
       {46.5, 47.5, 48.5},
       {50.5, 51.5, 52.5}},
    }, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 3, 3, 3}));
  }
  
  TEST_F(ModulesTest, MaxUnpool1d) {
    auto indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
    auto x = torch::tensor({{{2, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto model = MaxUnpool1d{3};
    auto y = model->forward(x, indices);
  
    ASSERT_EQ(y.dim(), 3);
    ASSERT_TRUE(torch::allclose(y,
      torch::tensor({{{0, 2, 0, 4, 5, 0, 0, 0, 0}}}, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 9}));
  
    indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
    x = torch::tensor({{{2, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
    model = MaxUnpool1d{MaxUnpool1dOptions(3).stride(2).padding(1)};
    y = model->forward(x, indices, std::vector<int64_t>({1, 1, 5}));
  
    ASSERT_EQ(y.dim(), 3);
    ASSERT_TRUE(torch::allclose(y,
      torch::tensor({{{0, 2, 0, 4, 5}}}, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 5}));
  }
  
  TEST_F(ModulesTest, MaxPool1d_MaxUnpool1d) {
    MaxPool1d pool {MaxPool1dOptions(2).stride(2)};
    MaxUnpool1d unpool {MaxUnpool1dOptions(2).stride(2)};
    auto input = torch::tensor({{{1, 2, 3, 4, 5, 6, 7, 8}}}, torch::kFloat);
    torch::Tensor output, indices;
    std::tie(output, indices) = pool->forward_with_indices(input);
    ASSERT_TRUE(torch::allclose(
      unpool(output, indices),
      torch::tensor({{{0, 2, 0, 4, 0, 6, 0, 8}}} , torch::kFloat)));
  
    // Example showcasing the use of output_size
    input = torch::tensor({{{1, 2, 3, 4, 5, 6, 7, 8, 9}}}, torch::kFloat);
    std::tie(output, indices) = pool->forward_with_indices(input);
    ASSERT_TRUE(torch::allclose(
      unpool(output, indices, input.sizes().vec()),
      torch::tensor({{{0, 2, 0, 4, 0, 6, 0, 8, 0}}} , torch::kFloat)));
    ASSERT_TRUE(torch::allclose(
      unpool(output, indices),
      torch::tensor({{{0, 2, 0, 4, 0, 6, 0, 8}}} , torch::kFloat)));
  }
  
  TEST_F(ModulesTest, MaxUnpool2d) {
    auto indices = torch::tensor({
    {{{ 6,  8,  9},
      {16, 18, 19},
      {21, 23, 24}}},
    {{{ 6,  8,  9},
      {16, 18, 19},
      {21, 23, 24}}}}, torch::kLong);
    auto x = torch::tensor({
    {{{ 6,  8,  9},
      {16, 18, 19},
      {21, 23, 24}}},
    {{{31, 33, 34},
      {41, 43, 44},
      {46, 48, 49}}}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto model = MaxUnpool2d{MaxUnpool2dOptions(3).stride(2).padding(1)};
    auto y = model->forward(x, indices);
  
    ASSERT_EQ(y.dim(), 4);
    ASSERT_TRUE(torch::allclose(y, torch::tensor(
     {{{{ 0,  0,  0,  0,  0},
        { 0,  6,  0,  8,  9},
        { 0,  0,  0,  0,  0},
        { 0, 16,  0, 18, 19},
        { 0, 21,  0, 23, 24}}},
      {{{ 0,  0,  0,  0,  0},
        { 0, 31,  0, 33, 34},
        { 0,  0,  0,  0,  0},
        { 0, 41,  0, 43, 44},
        { 0, 46,  0, 48, 49}}}} , torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 1, 5, 5}));
  }
  
  TEST_F(ModulesTest, MaxPool2d_MaxUnpool2d) {
    MaxPool2d pool {MaxPool2dOptions(2).stride(2)};
    MaxUnpool2d unpool {MaxUnpool2dOptions(2).stride(2)};
    auto input = torch::tensor({{{{ 1,  2,  3,  4},
                                  { 5,  6,  7,  8},
                                  { 9, 10, 11, 12},
                                  {13, 14, 15, 16}}}}, torch::kFloat);
    torch::Tensor output, indices;
    std::tie(output, indices) = pool->forward_with_indices(input);
    ASSERT_TRUE(torch::allclose(
      unpool(output, indices),
      torch::tensor({{{{ 0,  0, 0,  0},
                       { 0,  6, 0,  8},
                       { 0,  0, 0,  0},
                       { 0, 14, 0, 16}}}} , torch::kFloat)));
  
    ASSERT_TRUE(torch::allclose(
      unpool(output, indices, std::vector<int64_t>{1, 1, 5, 5}),
      torch::tensor({{{{ 0, 0, 0,  0, 0},
                       { 6, 0, 8,  0, 0},
                       { 0, 0, 0, 14, 0},
                       { 16, 0, 0, 0, 0},
                       { 0, 0, 0,  0, 0}}}}, torch::kFloat)));
  }
  
  TEST_F(ModulesTest, MaxUnpool3d) {
    auto indices = torch::tensor({{{{{26}}}}}, torch::kLong);
    auto x = torch::tensor({{{{{26}}}}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto model = MaxUnpool3d{3};
    auto y = model->forward(x, indices);
  
    ASSERT_EQ(y.dim(), 5);
    ASSERT_TRUE(torch::allclose(y, torch::tensor(
     {{{{{ 0,  0,  0},
         { 0,  0,  0},
         { 0,  0,  0}},
        {{ 0,  0,  0},
         { 0,  0,  0},
         { 0,  0,  0}},
        {{ 0,  0,  0},
         { 0,  0,  0},
         { 0,  0, 26}}}}}, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3, 3, 3}));
  }
  
  TEST_F(ModulesTest, MaxUnpool3dOutputSize) {
    auto indices = torch::tensor(
      {{{{{21, 23},
          {29, 31}},
         {{53, 55},
          {61, 63}}}}}, torch::kLong);
      auto x = torch::tensor(
      {{{{{21, 23},
          {29, 31}},
         {{53, 55},
          {61, 63}}}}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto model = MaxUnpool3d{MaxUnpool3dOptions(3).stride(2).padding(1)};
    auto y = model->forward(x, indices, std::vector<int64_t>({1, 1, 4, 4, 4}));
  
    ASSERT_EQ(y.dim(), 5);
    ASSERT_TRUE(torch::allclose(y, torch::tensor(
     {{{{{ 0,  0,  0,  0},
         { 0,  0,  0,  0},
         { 0,  0,  0,  0},
         { 0,  0,  0,  0}},
        {{ 0,  0,  0,  0},
         { 0, 21,  0, 23},
         { 0,  0,  0,  0},
         { 0, 29,  0, 31}},
        {{ 0,  0,  0,  0},
         { 0,  0,  0,  0},
         { 0,  0,  0,  0},
         { 0,  0,  0,  0}},
        {{ 0,  0,  0,  0},
         { 0, 53,  0, 55},
         { 0,  0,  0,  0},
         { 0, 61,  0, 63}}}}}, torch::kFloat)));
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 4, 4, 4}));
  }
  
  TEST_F(ModulesTest, MaxPool3d_MaxUnpool3d) {
    MaxPool3d pool {MaxPool3dOptions(3).stride(2)};
    MaxUnpool3d unpool {MaxUnpool3dOptions(3).stride(2)};
    auto input = torch::randn({20, 16, 51, 33, 15});
    torch::Tensor output, indices;
    std::tie(output, indices) = pool->forward_with_indices(input);
    auto unpooled_output = unpool(output, indices);
    ASSERT_EQ(unpooled_output.sizes(), std::vector<int64_t>({20, 16, 51, 33, 15}));
  }
  
  TEST_F(ModulesTest, Linear) {
    {
      Linear model(5, 2);
      auto x = torch::randn({10, 5}, torch::requires_grad());
      auto y = model(x);
      torch::Tensor s = y.sum();
  
      s.backward();
      ASSERT_EQ(y.ndimension(), 2);
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_EQ(y.size(0), 10);
      ASSERT_EQ(y.size(1), 2);
  
      ASSERT_EQ(model->weight.grad().numel(), 2 * 5);
  
      auto y_exp = torch::addmm(model->bias, x, model->weight.t());
      ASSERT_TRUE(torch::allclose(y, y_exp));
    }
    {
      Linear model(LinearOptions(5, 2).bias(false));
      auto x = torch::randn({10, 5}, torch::requires_grad());
      auto y = model(x);
      torch::Tensor s = y.sum();
  
      s.backward();
      ASSERT_EQ(y.ndimension(), 2);
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_EQ(y.size(0), 10);
      ASSERT_EQ(y.size(1), 2);
  
      ASSERT_EQ(model->weight.grad().numel(), 2 * 5);
  
      auto y_exp = torch::mm(x, model->weight.t());
      ASSERT_TRUE(torch::allclose(y, y_exp));
    }
  }
  
  TEST_F(ModulesTest, LocalResponseNorm) {
    {
      LocalResponseNorm model(LocalResponseNormOptions(2));
      const auto x = torch::arange(100., 136, torch::requires_grad()).reshape({2, 3, 3, 2});
      auto y = model(x);
      const auto y_exp = torch::tensor(
        {{{{73.7788, 74.1462},
            {74.5031, 74.8572},
            {75.2010, 75.5420}},
  
           {{61.6057, 61.7227},
            {61.8347, 61.9418},
            {62.0441, 62.1418}},
  
           {{62.2349, 62.3235},
            {62.4077, 62.4877},
            {62.5635, 62.6353}}},
  
          {{{79.3915, 79.6491},
            {79.8978, 80.1446},
            {80.3827, 80.6190}},
  
           {{63.0317, 63.0742},
            {63.1135, 63.1496},
            {63.1826, 63.2126}},
  
           {{63.2396, 63.2637},
            {63.2850, 63.3036},
            {63.3195, 63.3328}}}},
        torch::kFloat
      );
      torch::Tensor s = y.sum();
  
      s.backward();
      ASSERT_EQ(y.ndimension(), 4);
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_EQ(y.sizes(), x.sizes());
      ASSERT_TRUE(torch::allclose(y, y_exp, 1e-4, 1e-7));
    }
  }
  
  TEST_F(ModulesTest, LayerNorm) {
    LayerNorm model(LayerNormOptions({2, 2}).eps(2e-5));
    auto x = torch::randn({2, 2}, torch::requires_grad());
    auto y = model(x);
    auto y_exp = torch::layer_norm(x, {2, 2}, model->weight, model->bias, 2e-5);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 2);
    ASSERT_EQ(s.ndimension(), 0);
    for (auto i = 0; i < 2; i++) {
      ASSERT_EQ(y.size(i), 2);
    }
  
    ASSERT_EQ(model->weight.grad().numel(), 2 * 2);
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, GroupNorm) {
    GroupNorm model(GroupNormOptions(2, 2).eps(2e-5));
    auto x = torch::randn({2, 2}, torch::requires_grad());
    auto y = model(x);
    auto y_exp = torch::group_norm(x, 2, model->weight, model->bias, 2e-5);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 2);
    ASSERT_EQ(s.ndimension(), 0);
    for (auto i = 0; i < 2; i++) {
      ASSERT_EQ(y.size(i), 2);
    }
  
    ASSERT_EQ(model->weight.grad().numel(), 2);
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, Bilinear) {
    Bilinear model(5, 3, 2);
    auto x1 = torch::randn({10, 5}, torch::requires_grad());
    auto x2 = torch::randn({10, 3}, torch::requires_grad());
    auto y = model(x1, x2);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 2);
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.size(0), 10);
    ASSERT_EQ(y.size(1), 2);
  
    ASSERT_EQ(model->weight.grad().numel(), 2 * 5 * 3);
  }
  
  TEST_F(ModulesTest, Fold) {
    {
      Fold model(FoldOptions({3, 2}, {2, 2}));
      auto input = torch::ones({1, 3 * 2 * 2, 2}, torch::requires_grad());
      auto output = model(input);
      auto expected = torch::tensor(
          {{{{1.0, 1.0}, {2.0, 2.0}, {1.0, 1.0}},
            {{1.0, 1.0}, {2.0, 2.0}, {1.0, 1.0}},
            {{1.0, 1.0}, {2.0, 2.0}, {1.0, 1.0}}}},
          torch::kFloat);
      auto s = output.sum();
      s.backward();
  
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 3, 3, 2}));
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      // input wrong dimension
      Fold model(FoldOptions({8, 8}, {3, 3}));
      ASSERT_THROWS_WITH(
          model(torch::randn({1, 3, 16, 16})),
          "Input Error: Only 3D input Tensors are supported (got 4D)");
    }
  }
  
  TEST_F(ModulesTest, Unfold) {
    {
      Unfold model(UnfoldOptions({2, 2}).padding(1).stride(2));
      auto input = torch::arange(2., 14, torch::requires_grad()).view({1, 2, 2, 3});
      auto output = model(input);
      auto expected = torch::tensor(
          {{{0.0, 0.0, 0.0, 6.0},
            {0.0, 0.0, 5.0, 7.0},
            {0.0, 3.0, 0.0, 0.0},
            {2.0, 4.0, 0.0, 0.0},
            {0.0, 0.0, 0.0, 12.0},
            {0.0, 0.0, 11.0, 13.0},
            {0.0, 9.0, 0.0, 0.0},
            {8.0, 10.0, 0.0, 0.0}}},
          torch::kFloat);
      auto s = output.sum();
      s.backward();
  
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 8, 4}));
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      // input wrong dimension
      Unfold model(UnfoldOptions({2, 4}));
      ASSERT_THROWS_WITH(
          model(torch::randn({1, 5, 2})),
          "Input Error: Only 4D input Tensors are supported (got 3D)");
    }
    {
      // calculated output shape is too small
      Unfold model(UnfoldOptions({2, 3}));
      ASSERT_THROWS_WITH(
          model(torch::randn({1, 2, 2, 2})),
          "Given input with spatial size (2, 2), kernel_size=(2, 3), "
          "dilation=(1, 1), padding=(0, 0), calculated shape of the array of "
          "sliding blocks as (1, 0), which is too small (non-positive).");
    }
  }
  
  TEST_F(ModulesTest, SimpleContainer) {
    auto model = std::make_shared<SimpleContainer>();
    auto l1 = model->add(Linear(10, 3), "l1");
    auto l2 = model->add(Linear(3, 5), "l2");
    auto l3 = model->add(Linear(5, 100), "l3");
  
    auto x = torch::randn({1000, 10}, torch::requires_grad());
    x = l1(x).clamp_min(0);
    x = l2(x).clamp_min(0);
    x = l3(x).clamp_min(0);
  
    x.backward(torch::ones_like(x));
    ASSERT_EQ(x.ndimension(), 2);
    ASSERT_EQ(x.size(0), 1000);
    ASSERT_EQ(x.size(1), 100);
    ASSERT_EQ(x.min().item<float>(), 0);
  }
  
  TEST_F(ModulesTest, EmbeddingBasic) {
    const int64_t dict_size = 10;
    Embedding model(dict_size, 2);
    ASSERT_TRUE(model->named_parameters().contains("weight"));
    ASSERT_EQ(model->weight.ndimension(), 2);
    ASSERT_EQ(model->weight.size(0), dict_size);
    ASSERT_EQ(model->weight.size(1), 2);
  
    // Cannot get gradients to change indices (input) - only for embedding
    // params
    auto x = torch::full({10}, dict_size - 1, torch::kInt64);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 2);
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.size(0), 10);
    ASSERT_EQ(y.size(1), 2);
  
    ASSERT_EQ(model->weight.grad().numel(), 2 * dict_size);
  }
  
  TEST_F(ModulesTest, EmbeddingList) {
    Embedding model(6, 4);
    auto x = torch::full({2, 3}, 5, torch::kInt64);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_EQ(y.size(0), 2);
    ASSERT_EQ(y.size(1), 3);
    ASSERT_EQ(y.size(2), 4);
  }
  
  TEST_F(ModulesTest, EmbeddingFromPretrained) {
    auto weight = torch::tensor({{1., 2.3, 3.}, {4., 5.1, 6.3}});
    Embedding embedding = torch::nn::Embedding::from_pretrained(weight);
    auto input = torch::tensor({1}, torch::kLong);
    ASSERT_TRUE(torch::allclose(embedding(input), torch::tensor({4.0000, 5.1000, 6.3000})));
  }
  
  TEST_F(ModulesTest, EmbeddingBagFromPretrained) {
    auto weight = torch::tensor({{1., 2.3, 3.}, {4., 5.1, 6.3}});
    EmbeddingBag embeddingbag = torch::nn::EmbeddingBag::from_pretrained(weight);
    auto input = torch::zeros({{1, 2}}, torch::kLong);
    input[0] = torch::tensor({1, 0});
    ASSERT_TRUE(torch::allclose(embeddingbag(input), torch::tensor({2.5000, 3.7000, 4.6500})));
  }
  
  TEST_F(ModulesTest, AlphaDropout) {
    AlphaDropout alpha_dropout(0.5);
    torch::Tensor x = torch::ones(100, torch::requires_grad());
    torch::Tensor y = alpha_dropout(x);
  
    y.backward(torch::ones_like(y));
  
    ASSERT_EQ(y.ndimension(), 1);
    ASSERT_EQ(y.size(0), 100);
    ASSERT_LT(y.sum().item<float>(), 130); // Probably
    ASSERT_GT(y.sum().item<float>(), 40); // Probably
  
    alpha_dropout->eval();
    y = alpha_dropout(x);
  
    ASSERT_EQ(y.sum().item<float>(), 100);
  }
  
  TEST_F(ModulesTest, FeatureAlphaDropout) {
    FeatureAlphaDropout feature_alpha_dropout(0.5);
    torch::Tensor x = torch::ones({10, 10}, torch::requires_grad());
    torch::Tensor y = feature_alpha_dropout(x);
  
    y.backward(torch::ones_like(y));
  
    ASSERT_EQ(y.ndimension(), 2);
    ASSERT_EQ(y.size(0), 10);
    ASSERT_EQ(y.size(1), 10);
    ASSERT_LT(y.sum().item<float>(), 130); // Probably
    ASSERT_GT(y.sum().item<float>(), 40); // Probably
  
    feature_alpha_dropout->eval();
    y = feature_alpha_dropout(x);
  
    ASSERT_EQ(y.sum().item<float>(), 100);
  }
  
  TEST_F(ModulesTest, Dropout) {
    for (const auto inplace : {false, true}) {
      Dropout dropout(DropoutOptions(0.5).inplace(inplace));
      torch::Tensor x = torch::ones(100);
      if (!inplace) {
        x.requires_grad_(true);
      }
      torch::Tensor y = dropout(x);
  
      ASSERT_EQ(y.ndimension(), 1);
      ASSERT_EQ(y.size(0), 100);
      ASSERT_LT(y.sum().item<float>(), 130); // Probably
      ASSERT_GT(y.sum().item<float>(), 70); // Probably
      if (inplace) {
        ASSERT_TRUE(y.allclose(x));
      } else {
        y.backward(torch::ones_like(y));
      }
  
      dropout->eval();
      y = dropout(torch::ones(100));
      ASSERT_EQ(y.sum().item<float>(), 100);
    }
  }
  
  TEST_F(ModulesTest, Dropout2d) {
    for (const auto inplace : {false, true}) {
      Dropout2d dropout(Dropout2dOptions(0.5).inplace(inplace));
      torch::Tensor x = torch::ones({10, 10});
      if (!inplace) {
        x.requires_grad_(true);
      }
      torch::Tensor y = dropout(x);
  
      ASSERT_EQ(y.ndimension(), 2);
      ASSERT_EQ(y.size(0), 10);
      ASSERT_EQ(y.size(1), 10);
      ASSERT_LT(y.sum().item<float>(), 130); // Probably
      ASSERT_GT(y.sum().item<float>(), 70); // Probably
      if (inplace) {
        ASSERT_TRUE(y.allclose(x));
      } else {
        y.backward(torch::ones_like(y));
      }
  
      dropout->eval();
      y = dropout(torch::ones({10, 10}));
      ASSERT_EQ(y.sum().item<float>(), 100);
    }
  }
  
  TEST_F(ModulesTest, Dropout3d) {
    for (const auto inplace : {false, true}) {
      Dropout3d dropout(Dropout3dOptions(0.5).inplace(inplace));
      torch::Tensor x = torch::ones({4, 5, 5});
      if (!inplace) {
        x.requires_grad_(true);
      }
      torch::Tensor y = dropout(x);
  
      ASSERT_EQ(y.ndimension(), 3);
      ASSERT_EQ(y.size(0), 4);
      ASSERT_EQ(y.size(1), 5);
      ASSERT_EQ(y.size(1), 5);
      ASSERT_LT(y.sum().item<float>(), 130); // Probably
      ASSERT_GT(y.sum().item<float>(), 70); // Probably
      if (inplace) {
        ASSERT_TRUE(y.allclose(x));
      } else {
        y.backward(torch::ones_like(y));
      }
  
      dropout->eval();
      y = dropout(torch::ones({4, 5, 5}));
      ASSERT_EQ(y.sum().item<float>(), 100);
    }
  }
  
  TEST_F(ModulesTest, Parameters) {
    auto model = std::make_shared<NestedModel>();
    auto parameters = model->named_parameters();
    ASSERT_EQ(parameters["param"].size(0), 3);
    ASSERT_EQ(parameters["param"].size(1), 2);
    ASSERT_EQ(parameters["param"].size(2), 21);
    ASSERT_EQ(parameters["l1.bias"].size(0), 20);
    ASSERT_EQ(parameters["l1.weight"].size(0), 20);
    ASSERT_EQ(parameters["l1.weight"].size(1), 5);
    ASSERT_EQ(parameters["test.l1.bias"].size(0), 3);
    ASSERT_EQ(parameters["test.l1.weight"].size(0), 3);
    ASSERT_EQ(parameters["test.l1.weight"].size(1), 10);
    ASSERT_EQ(parameters["test.l2.bias"].size(0), 5);
    ASSERT_EQ(parameters["test.l2.weight"].size(0), 5);
    ASSERT_EQ(parameters["test.l2.weight"].size(1), 3);
    ASSERT_EQ(parameters["test.l3.bias"].size(0), 100);
    ASSERT_EQ(parameters["test.l3.weight"].size(0), 100);
    ASSERT_EQ(parameters["test.l3.weight"].size(1), 5);
  }
  
  TEST_F(ModulesTest, FunctionalCallsSuppliedFunction) {
    bool was_called = false;
    auto functional = Functional([&was_called](torch::Tensor input) {
      was_called = true;
      return input;
    });
    auto output = functional(torch::ones(5, torch::requires_grad()));
    ASSERT_TRUE(was_called);
    ASSERT_TRUE(output.equal(torch::ones(5, torch::requires_grad())));
  
    was_called = false;
    // Use the call operator overload here.
    output = functional(torch::ones(5, torch::requires_grad()));
    ASSERT_TRUE(was_called);
    ASSERT_TRUE(output.equal(torch::ones(5, torch::requires_grad())));
  }
  
  TEST_F(ModulesTest, FunctionalWithTorchFunction) {
    auto functional = Functional(torch::relu);
    ASSERT_EQ(functional(torch::ones({})).item<float>(), 1);
    ASSERT_EQ(functional(torch::ones({})).item<float>(), 1);
    ASSERT_EQ(functional(torch::ones({}) * -1).item<float>(), 0);
  }
  
  TEST_F(ModulesTest, FunctionalArgumentBinding) {
    auto functional =
        Functional(torch::elu, /*alpha=*/1, /*scale=*/0, /*input_scale=*/1);
    ASSERT_EQ(functional(torch::ones({})).item<float>(), 0);
  }
  
  TEST_F(ModulesTest, BatchNorm1dStateful) {
    BatchNorm1d bn(5);
  
    ASSERT_TRUE(bn->options.track_running_stats());
  
    ASSERT_TRUE(bn->running_mean.defined());
    ASSERT_EQ(bn->running_mean.dim(), 1);
    ASSERT_EQ(bn->running_mean.size(0), 5);
  
    ASSERT_TRUE(bn->running_var.defined());
    ASSERT_EQ(bn->running_var.dim(), 1);
    ASSERT_EQ(bn->running_var.size(0), 5);
  
    ASSERT_TRUE(bn->num_batches_tracked.defined());
    ASSERT_EQ(bn->num_batches_tracked.dim(), 0);
  
    ASSERT_TRUE(bn->options.affine());
  
    ASSERT_TRUE(bn->weight.defined());
    ASSERT_EQ(bn->weight.dim(), 1);
    ASSERT_EQ(bn->weight.size(0), 5);
  
    ASSERT_TRUE(bn->bias.defined());
    ASSERT_EQ(bn->bias.dim(), 1);
    ASSERT_EQ(bn->bias.size(0), 5);
  }
  
  TEST_F(ModulesTest, BatchNorm1dStateless) {
    BatchNorm1d bn(BatchNorm1dOptions(5).track_running_stats(false).affine(false));
  
    ASSERT_FALSE(bn->running_mean.defined());
    ASSERT_FALSE(bn->running_var.defined());
    ASSERT_FALSE(bn->num_batches_tracked.defined());
    ASSERT_FALSE(bn->weight.defined());
    ASSERT_FALSE(bn->bias.defined());
  }
  
  TEST_F(ModulesTest, BatchNorm1d) {
    BatchNorm1d bn(5);
    bn->eval();
  
    auto input = torch::arange(2. * 5 * 2).view({2, 5, 2}).requires_grad_();
    auto output = bn->forward(input);
    auto expected = torch::tensor({{{ 0.0000,  1.0000},
                                    { 2.0000,  3.0000},
                                    { 4.0000,  5.0000},
                                    { 6.0000,  7.0000},
                                    { 8.0000,  9.0000}},
                                   {{10.0000, 10.9999},
                                    {11.9999, 12.9999},
                                    {13.9999, 14.9999},
                                    {15.9999, 16.9999},
                                    {17.9999, 18.9999}}});
    ASSERT_TRUE(output.allclose(expected));
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, BatchNorm2dStateful) {
    BatchNorm2d bn(5);
  
    ASSERT_TRUE(bn->options.track_running_stats());
  
    ASSERT_TRUE(bn->running_mean.defined());
    ASSERT_EQ(bn->running_mean.dim(), 1);
    ASSERT_EQ(bn->running_mean.size(0), 5);
  
    ASSERT_TRUE(bn->running_var.defined());
    ASSERT_EQ(bn->running_var.dim(), 1);
    ASSERT_EQ(bn->running_var.size(0), 5);
  
    ASSERT_TRUE(bn->num_batches_tracked.defined());
    ASSERT_EQ(bn->num_batches_tracked.dim(), 0);
  
    ASSERT_TRUE(bn->options.affine());
  
    ASSERT_TRUE(bn->weight.defined());
    ASSERT_EQ(bn->weight.dim(), 1);
    ASSERT_EQ(bn->weight.size(0), 5);
  
    ASSERT_TRUE(bn->bias.defined());
    ASSERT_EQ(bn->bias.dim(), 1);
    ASSERT_EQ(bn->bias.size(0), 5);
  }
  
  TEST_F(ModulesTest, BatchNorm2dStateless) {
    BatchNorm2d bn(BatchNorm2dOptions(5).track_running_stats(false).affine(false));
  
    ASSERT_FALSE(bn->running_mean.defined());
    ASSERT_FALSE(bn->running_var.defined());
    ASSERT_FALSE(bn->num_batches_tracked.defined());
    ASSERT_FALSE(bn->weight.defined());
    ASSERT_FALSE(bn->bias.defined());
  }
  
  TEST_F(ModulesTest, BatchNorm2d) {
    BatchNorm2d bn(5);
    bn->eval();
  
    auto input = torch::arange(2. * 5 * 2 * 2).view({2, 5, 2, 2}).requires_grad_();
    auto output = bn->forward(input);
    auto expected = torch::tensor({{{{ 0.0000,  1.0000},
                                     { 2.0000,  3.0000}},
                                    {{ 4.0000,  5.0000},
                                     { 6.0000,  7.0000}},
                                    {{ 8.0000,  9.0000},
                                     {10.0000, 10.9999}},
                                    {{11.9999, 12.9999},
                                     {13.9999, 14.9999}},
                                    {{15.9999, 16.9999},
                                     {17.9999, 18.9999}}},
                                   {{{19.9999, 20.9999},
                                     {21.9999, 22.9999}},
                                    {{23.9999, 24.9999},
                                     {25.9999, 26.9999}},
                                    {{27.9999, 28.9999},
                                     {29.9998, 30.9998}},
                                    {{31.9998, 32.9998},
                                     {33.9998, 34.9998}},
                                    {{35.9998, 36.9998},
                                     {37.9998, 38.9998}}}});
    ASSERT_TRUE(output.allclose(expected));
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, BatchNorm3dStateful) {
    BatchNorm3d bn(5);
  
    ASSERT_TRUE(bn->options.track_running_stats());
  
    ASSERT_TRUE(bn->running_mean.defined());
    ASSERT_EQ(bn->running_mean.dim(), 1);
    ASSERT_EQ(bn->running_mean.size(0), 5);
  
    ASSERT_TRUE(bn->running_var.defined());
    ASSERT_EQ(bn->running_var.dim(), 1);
    ASSERT_EQ(bn->running_var.size(0), 5);
  
    ASSERT_TRUE(bn->num_batches_tracked.defined());
    ASSERT_EQ(bn->num_batches_tracked.dim(), 0);
  
    ASSERT_TRUE(bn->options.affine());
  
    ASSERT_TRUE(bn->weight.defined());
    ASSERT_EQ(bn->weight.dim(), 1);
    ASSERT_EQ(bn->weight.size(0), 5);
  
    ASSERT_TRUE(bn->bias.defined());
    ASSERT_EQ(bn->bias.dim(), 1);
    ASSERT_EQ(bn->bias.size(0), 5);
  }
  
  TEST_F(ModulesTest, BatchNorm3dStateless) {
    BatchNorm3d bn(BatchNorm3dOptions(5).track_running_stats(false).affine(false));
  
    ASSERT_FALSE(bn->running_mean.defined());
    ASSERT_FALSE(bn->running_var.defined());
    ASSERT_FALSE(bn->num_batches_tracked.defined());
    ASSERT_FALSE(bn->weight.defined());
    ASSERT_FALSE(bn->bias.defined());
  }
  
  TEST_F(ModulesTest, BatchNorm3d) {
    BatchNorm3d bn(5);
    bn->eval();
  
    auto input = torch::arange(2. * 5 * 2 * 2 * 2).view({2, 5, 2, 2, 2}).requires_grad_();
    auto output = bn->forward(input);
    auto expected = torch::tensor({{{{{ 0.0000,  1.0000},
                                      { 2.0000,  3.0000}},
                                     {{ 4.0000,  5.0000},
                                      { 6.0000,  7.0000}}},
                                    {{{ 8.0000,  9.0000},
                                      {10.0000, 10.9999}},
                                     {{11.9999, 12.9999},
                                      {13.9999, 14.9999}}},
                                    {{{15.9999, 16.9999},
                                      {17.9999, 18.9999}},
                                     {{19.9999, 20.9999},
                                      {21.9999, 22.9999}}},
                                    {{{23.9999, 24.9999},
                                      {25.9999, 26.9999}},
                                     {{27.9999, 28.9999},
                                      {29.9998, 30.9998}}},
                                    {{{31.9998, 32.9998},
                                      {33.9998, 34.9998}},
                                     {{35.9998, 36.9998},
                                      {37.9998, 38.9998}}}},
                                   {{{{39.9998, 40.9998},
                                      {41.9998, 42.9998}},
                                     {{43.9998, 44.9998},
                                      {45.9998, 46.9998}}},
                                    {{{47.9998, 48.9998},
                                      {49.9997, 50.9997}},
                                     {{51.9997, 52.9997},
                                      {53.9997, 54.9997}}},
                                    {{{55.9997, 56.9997},
                                      {57.9997, 58.9997}},
                                     {{59.9997, 60.9997},
                                      {61.9997, 62.9997}}},
                                    {{{63.9997, 64.9997},
                                      {65.9997, 66.9997}},
                                     {{67.9997, 68.9997},
                                      {69.9996, 70.9996}}},
                                    {{{71.9996, 72.9996},
                                      {73.9996, 74.9996}},
                                     {{75.9996, 76.9996},
                                      {77.9996, 78.9996}}}}});
    ASSERT_TRUE(output.allclose(expected));
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, InstanceNorm1dStateful) {
    InstanceNorm1d instance_norm(InstanceNorm1dOptions(5).track_running_stats(true).affine(true));
  
    ASSERT_TRUE(instance_norm->options.track_running_stats());
  
    ASSERT_TRUE(instance_norm->running_mean.defined());
    ASSERT_EQ(instance_norm->running_mean.dim(), 1);
    ASSERT_EQ(instance_norm->running_mean.size(0), 5);
  
    ASSERT_TRUE(instance_norm->running_var.defined());
    ASSERT_EQ(instance_norm->running_var.dim(), 1);
    ASSERT_EQ(instance_norm->running_var.size(0), 5);
  
    ASSERT_TRUE(instance_norm->num_batches_tracked.defined());
    ASSERT_EQ(instance_norm->num_batches_tracked.dim(), 0);
  
    ASSERT_TRUE(instance_norm->options.affine());
  
    ASSERT_TRUE(instance_norm->weight.defined());
    ASSERT_EQ(instance_norm->weight.dim(), 1);
    ASSERT_EQ(instance_norm->weight.size(0), 5);
  
    ASSERT_TRUE(instance_norm->bias.defined());
    ASSERT_EQ(instance_norm->bias.dim(), 1);
    ASSERT_EQ(instance_norm->bias.size(0), 5);
  }
  
  TEST_F(ModulesTest, InstanceNorm1dStateless) {
    InstanceNorm1d instance_norm(InstanceNorm1dOptions(5).track_running_stats(false).affine(false));
  
    ASSERT_FALSE(instance_norm->running_mean.defined());
    ASSERT_FALSE(instance_norm->running_var.defined());
    ASSERT_FALSE(instance_norm->num_batches_tracked.defined());
    ASSERT_FALSE(instance_norm->weight.defined());
    ASSERT_FALSE(instance_norm->bias.defined());
  }
  
  TEST_F(ModulesTest, InstanceNorm1d) {
    InstanceNorm1d instance_norm(5);
    instance_norm->eval();
  
    auto input = torch::arange(2. * 5 * 2).view({2, 5, 2}).requires_grad_();
    auto output = instance_norm->forward(input);
    auto expected = torch::tensor({{{-1.0000, 1.0000},
                                    {-1.0000, 1.0000},
                                    {-1.0000, 1.0000},
                                    {-1.0000, 1.0000},
                                    {-1.0000, 1.0000}},
                                   {{-1.0000, 1.0000},
                                    {-1.0000, 1.0000},
                                    {-1.0000, 1.0000},
                                    {-1.0000, 1.0000},
                                    {-1.0000, 1.0000}}});
    ASSERT_TRUE(output.allclose(expected, 1e-3));
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, InstanceNorm2dStateful) {
    InstanceNorm2d instance_norm(InstanceNorm2dOptions(5).track_running_stats(true).affine(true));
  
    ASSERT_TRUE(instance_norm->options.track_running_stats());
  
    ASSERT_TRUE(instance_norm->running_mean.defined());
    ASSERT_EQ(instance_norm->running_mean.dim(), 1);
    ASSERT_EQ(instance_norm->running_mean.size(0), 5);
  
    ASSERT_TRUE(instance_norm->running_var.defined());
    ASSERT_EQ(instance_norm->running_var.dim(), 1);
    ASSERT_EQ(instance_norm->running_var.size(0), 5);
  
    ASSERT_TRUE(instance_norm->num_batches_tracked.defined());
    ASSERT_EQ(instance_norm->num_batches_tracked.dim(), 0);
  
    ASSERT_TRUE(instance_norm->options.affine());
  
    ASSERT_TRUE(instance_norm->weight.defined());
    ASSERT_EQ(instance_norm->weight.dim(), 1);
    ASSERT_EQ(instance_norm->weight.size(0), 5);
  
    ASSERT_TRUE(instance_norm->bias.defined());
    ASSERT_EQ(instance_norm->bias.dim(), 1);
    ASSERT_EQ(instance_norm->bias.size(0), 5);
  }
  
  TEST_F(ModulesTest, InstanceNorm2dStateless) {
    InstanceNorm2d instance_norm(InstanceNorm2dOptions(5).track_running_stats(false).affine(false));
  
    ASSERT_FALSE(instance_norm->running_mean.defined());
    ASSERT_FALSE(instance_norm->running_var.defined());
    ASSERT_FALSE(instance_norm->num_batches_tracked.defined());
    ASSERT_FALSE(instance_norm->weight.defined());
    ASSERT_FALSE(instance_norm->bias.defined());
  }
  
  TEST_F(ModulesTest, InstanceNorm2d) {
    InstanceNorm2d instance_norm(5);
    instance_norm->eval();
  
    auto input = torch::arange(2. * 5 * 2 * 2).view({2, 5, 2, 2}).requires_grad_();
    auto output = instance_norm->forward(input);
    auto expected = torch::tensor({{{{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}},
                                    {{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}},
                                    {{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}},
                                    {{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}},
                                    {{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}}},
                                   {{{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}},
                                    {{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}},
                                    {{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}},
                                    {{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}},
                                    {{-1.3416, -0.4472},
                                     { 0.4472,  1.3416}}}});
    ASSERT_TRUE(output.allclose(expected, 1e-3));
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, InstanceNorm3dStateful) {
    InstanceNorm3d instance_norm(InstanceNorm3dOptions(5).track_running_stats(true).affine(true));
  
    ASSERT_TRUE(instance_norm->options.track_running_stats());
  
    ASSERT_TRUE(instance_norm->running_mean.defined());
    ASSERT_EQ(instance_norm->running_mean.dim(), 1);
    ASSERT_EQ(instance_norm->running_mean.size(0), 5);
  
    ASSERT_TRUE(instance_norm->running_var.defined());
    ASSERT_EQ(instance_norm->running_var.dim(), 1);
    ASSERT_EQ(instance_norm->running_var.size(0), 5);
  
    ASSERT_TRUE(instance_norm->num_batches_tracked.defined());
    ASSERT_EQ(instance_norm->num_batches_tracked.dim(), 0);
  
    ASSERT_TRUE(instance_norm->options.affine());
  
    ASSERT_TRUE(instance_norm->weight.defined());
    ASSERT_EQ(instance_norm->weight.dim(), 1);
    ASSERT_EQ(instance_norm->weight.size(0), 5);
  
    ASSERT_TRUE(instance_norm->bias.defined());
    ASSERT_EQ(instance_norm->bias.dim(), 1);
    ASSERT_EQ(instance_norm->bias.size(0), 5);
  }
  
  TEST_F(ModulesTest, InstanceNorm3dStateless) {
    InstanceNorm3d instance_norm(InstanceNorm3dOptions(5).track_running_stats(false).affine(false));
  
    ASSERT_FALSE(instance_norm->running_mean.defined());
    ASSERT_FALSE(instance_norm->running_var.defined());
    ASSERT_FALSE(instance_norm->num_batches_tracked.defined());
    ASSERT_FALSE(instance_norm->weight.defined());
    ASSERT_FALSE(instance_norm->bias.defined());
  }
  
  TEST_F(ModulesTest, InstanceNorm3d) {
    InstanceNorm3d instance_norm(5);
    instance_norm->eval();
  
    auto input = torch::arange(2. * 5 * 2 * 2 * 2).view({2, 5, 2, 2, 2}).requires_grad_();
    auto output = instance_norm->forward(input);
    auto expected = torch::tensor({{{{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}},
                                    {{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}},
                                    {{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}},
                                    {{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}},
                                    {{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}}},
                                   {{{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}},
                                    {{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}},
                                    {{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}},
                                    {{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}},
                                    {{{-1.5275, -1.0911},
                                      {-0.6547, -0.2182}},
                                     {{ 0.2182,  0.6547},
                                      { 1.0911,  1.5275}}}}});
    ASSERT_TRUE(output.allclose(expected, 1e-3));
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, Linear_CUDA) {
    Linear model(5, 2);
    model->to(torch::kCUDA);
    auto x =
        torch::randn({10, 5}, torch::device(torch::kCUDA).requires_grad(true));
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 2);
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.size(0), 10);
    ASSERT_EQ(y.size(1), 2);
  
    ASSERT_EQ(model->weight.grad().numel(), 2 * 5);
  }
  
  TEST_F(ModulesTest, Linear2_CUDA) {
    Linear model(5, 2);
    model->to(torch::kCUDA);
    model->to(torch::kCPU);
    auto x = torch::randn({10, 5}, torch::requires_grad());
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(y.ndimension(), 2);
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_EQ(y.size(0), 10);
    ASSERT_EQ(y.size(1), 2);
  
    ASSERT_EQ(model->weight.grad().numel(), 2 * 5);
  }
  
  TEST_F(ModulesTest, L1Loss) {
    L1Loss loss;
    auto input = torch::randn({5,6}, torch::requires_grad());
    auto target = torch::empty({5,6}).random_(2);
    auto output = loss->forward(torch::sigmoid(input), target);
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(output.sizes(), std::vector<int64_t>());
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, MSELoss) {
    MSELoss loss;
    auto input = torch::randn({5,6}, torch::requires_grad());
    auto target = torch::empty({5,6}).random_(2);
    auto output = loss->forward(torch::sigmoid(input), target);
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(output.sizes(), torch::IntArrayRef());
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, BCELoss) {
    BCELoss loss;
    auto input = torch::randn({5,6}, torch::requires_grad());
    auto target = torch::empty({5,6}).random_(2);
    auto output = loss->forward(torch::sigmoid(input), target);
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(output.sizes(), torch::IntArrayRef());
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, KLDivLoss) {
    KLDivLoss loss;
    auto input = torch::randn({5,6}, torch::requires_grad());
    auto target = torch::empty({5,6}).random_(2);
    auto output = loss->forward(torch::sigmoid(input), target);
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(output.sizes(), torch::IntArrayRef());
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, HingeEmbeddingLoss) {
    HingeEmbeddingLoss loss(HingeEmbeddingLossOptions().margin(2));
    auto input = torch::tensor({{2, 22, 4}, {20, 10, 0}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({{2, 6, 4}, {1, 10, 0}}, torch::kFloat);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor({10}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, MultiMarginLoss) {
    auto weight = torch::tensor({0.3, 0.3, 0.4}, torch::kFloat);
    MultiMarginLoss loss(MultiMarginLossOptions().margin(2).weight(weight));
    auto input = torch::tensor({{0.2, 0.2, 0.6}, {0.1, 0.8, 0.1}, {0.9, 0.09, 0.01}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({2, 1, 0}, torch::kLong);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor({0.305556}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected, 1e-04));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, CosineEmbeddingLoss) {
    CosineEmbeddingLoss cos(CosineEmbeddingLossOptions().margin(0.5));
    auto input1 = torch::tensor({{2, 3, 4}, {6, 2, 4}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto input2 = torch::tensor({{2, 3, 5}, {9, 12, 0}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({1, -1});
    auto output = cos(input1, input2, target);
    auto expected = torch::tensor({0.1004}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected, 1e-4));
    ASSERT_EQ(input1.sizes(), input1.grad().sizes());
    ASSERT_EQ(input2.sizes(), input2.grad().sizes());
  }
  
  TEST_F(ModulesTest, SmoothL1LossDefaultOptions) {
    SmoothL1Loss loss;
    auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
    auto output = loss(input, target);
    auto expected = torch::tensor(0.0233335, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, HuberLossDefaultOptions) {
    HuberLoss loss;
    auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
    auto output = loss(input, target);
    auto expected = torch::tensor(0.0233335, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, MultiLabelMarginLossDefaultOptions) {
    MultiLabelMarginLoss loss;
    auto input = torch::tensor({{0.1, 0.2, 0.4, 0.8}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({{3, 0, -1, 1}}, torch::kLong);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor({0.8500}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, SmoothL1LossNoReduction) {
    SmoothL1Loss loss(/*reduction=*/torch::kNone);
    auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
    auto output = loss(input, target);
    auto expected = torch::tensor({0.005, 0.02, 0.045}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, HuberLossNoReduction) {
    HuberLoss loss(/*reduction=*/torch::kNone);
    auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
    auto output = loss(input, target);
    auto expected = torch::tensor({0.005, 0.02, 0.045}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, MultiLabelMarginLossNoReduction) {
    MultiLabelMarginLoss loss(torch::kNone);
    auto input = torch::tensor({{0.1, 0.2, 0.4, 0.8}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({{3, 0, -1, 1}}, torch::kLong);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor({0.8500}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, SmoothL1LossBeta) {
    auto options = SmoothL1LossOptions().beta(0.2);
    SmoothL1Loss loss(options);
    auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
    auto output = loss(input, target);
    auto expected = torch::tensor(0.108333, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, HuberLossDelta) {
    auto options = HuberLossOptions().delta(0.2);
    HuberLoss loss(options);
    auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
    auto output = loss(input, target);
    auto expected = torch::tensor(0.0216666, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, TripletMarginLoss) {
    TripletMarginLoss loss(TripletMarginLossOptions().margin(1.0));
    auto anchor = torch::tensor({{3., 3.}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto positive = torch::tensor({{2., 2.}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto negative = torch::tensor({{0., 0.}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto output = loss->forward(anchor, positive, negative);
    auto expected = torch::tensor({0.}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected, 1e-04));
    ASSERT_EQ(anchor.sizes(), anchor.grad().sizes());
  }
  
  TEST_F(ModulesTest, TripletMarginWithDistanceLossDefaultParity) {
    // Check that if we use torch::pairwise_distance with the default
    // TripletMarginLoss options as our distance function, the outputs
    // are equal (i.e., equal under defaults).
  
    std::vector<TripletMarginWithDistanceLossOptions::reduction_t>
        reductions = {torch::kSum, torch::kMean, torch::kNone};
    std::vector<float> margins = {0.5, 1.0, 1.5};
    std::vector<bool> swaps = {true, false};
  
    for (auto& reduction : reductions) {
      for (auto& margin : margins) {
        for (const auto swap : swaps) {
          auto anchor =
              torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
          auto positive =
              torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
          auto negative =
              torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
  
          auto basicOptions = TripletMarginLossOptions()
                                  .reduction(reduction)
                                  .margin(margin)
                                  .swap(swap);
          auto distanceOptions =
              TripletMarginWithDistanceLossOptions()
                  .reduction(reduction)
                  .margin(margin)
                  .swap(swap);
          TripletMarginLoss basicLoss(basicOptions);
          TripletMarginWithDistanceLoss distanceLoss(distanceOptions);
  
          auto basicOutput = basicLoss->forward(anchor, positive, negative);
          auto distanceOutput = distanceLoss->forward(anchor, positive, negative);
          auto basicOperatorOutput = basicLoss(anchor, positive, negative);
          auto distanceOperatorOutput = distanceLoss(anchor, positive, negative);
  
          ASSERT_TRUE(distanceOutput.allclose(basicOutput, 1e-6, 1e-6));
          ASSERT_TRUE(distanceOperatorOutput.allclose(distanceOutput, 1e-6, 1e-6));
          ASSERT_TRUE(distanceOperatorOutput.allclose(basicOperatorOutput, 1e-6, 1e-6));
  
          // handle for torch::kNone reduction
          auto sum = distanceOutput.sum();
          sum.backward();
          ASSERT_EQ(anchor.sizes(), anchor.grad().sizes());
          ASSERT_EQ(positive.sizes(), positive.grad().sizes());
          ASSERT_EQ(negative.sizes(), negative.grad().sizes());
        }
      }
    }
  }
  
  TEST_F(ModulesTest, TripletMarginWithDistanceLossFunctionalParity) {
    // Check for parity between F::triplet_margin_with_distance_loss and
    // TripletMarginWithDistanceLoss.
    auto pairwise_distance = [&](const torch::Tensor& x, const torch::Tensor& y) {
      return torch::pairwise_distance(x, y);
    };
    auto cosine_distance = [&](const torch::Tensor& x,
                                   const torch::Tensor& y) {
      return 1.0 - torch::cosine_similarity(x, y);
    };
    std::vector<TripletMarginWithDistanceLossOptions::distance_function_t>
        distance_functions = {pairwise_distance, cosine_distance};
  
    std::vector<TripletMarginWithDistanceLossOptions::reduction_t>
        reductions = {torch::kSum, torch::kMean, torch::kNone};
    std::vector<float> margins = {0.5, 1.0, 1.5};
    std::vector<bool> swaps = {true, false};
  
    for (auto& function : distance_functions) {
      for (auto& reduction : reductions) {
        for (auto& margin : margins) {
          for (const auto swap : swaps) {
            auto moduleOptions =
                TripletMarginWithDistanceLossOptions()
                    .distance_function(function)
                    .reduction(reduction)
                    .margin(margin)
                    .swap(swap);
            auto functionOptions =
                torch::nn::functional::TripletMarginWithDistanceLossFuncOptions()
                    .distance_function(function)
                    .reduction(reduction)
                    .margin(margin)
                    .swap(swap);
  
            auto anchor = torch::randn(
                {100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
            auto positive = torch::randn(
                {100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
            auto negative = torch::randn(
                {100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
  
            TripletMarginWithDistanceLoss distanceLoss(moduleOptions);
  
            auto moduleOutput = distanceLoss->forward(anchor, positive, negative);
            auto moduleOperatorOutput = distanceLoss(anchor, positive, negative);
            auto functionOutput = torch::nn::functional::triplet_margin_with_distance_loss(
              anchor, positive, negative, functionOptions);
  
            ASSERT_TRUE(moduleOutput.allclose(functionOutput, 1e-6, 1e-6));
            ASSERT_TRUE(moduleOperatorOutput.allclose(functionOutput, 1e-6, 1e-6));
          }
        }
      }
    }
  }
  
  TEST_F(ModulesTest, NLLLoss) {
    NLLLoss loss;
    auto input = torch::tensor({{-0.1315, -3.1315, -2.5315},
                                {-3.7038, -0.1038, -2.6038},
                                {-2.3422, -1.3422, -0.4422}},
                               torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({1, 0, 2}, torch::kLong);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor(2.4258, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected, 1e-04));
    ASSERT_TRUE(
      NLLLoss(NLLLossOptions().ignore_index(-100).reduction(torch::kMean))
        ->forward(input, target).allclose(expected, 1e-04));
  }
  
  TEST_F(ModulesTest, CrossEntropyLoss) {
    CrossEntropyLoss loss;
    auto input = torch::tensor({{3., 3.}, {2., 2.}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({0, 1}, torch::kLong);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor(0.6931, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected, 1e-04));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
    ASSERT_TRUE(
      CrossEntropyLoss(CrossEntropyLossOptions().ignore_index(-100).reduction(torch::kMean))
        ->forward(input, target).allclose(expected, 1e-04));
  }
  
  TEST_F(ModulesTest, CosineSimilarity) {
    CosineSimilarity cos(CosineSimilarityOptions().dim(1));
    auto input1 = torch::tensor({{1, 2, 3}, {4, 5, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto input2 = torch::tensor({{1, 8, 3}, {2, 1, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto output = cos->forward(input1, input2);
    auto expected = torch::tensor({0.8078, 0.8721}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected, 1e-04));
    ASSERT_EQ(input1.sizes(), input1.grad().sizes());
  }
  
  TEST_F(ModulesTest, SoftMarginLossDefaultOptions) {
    SoftMarginLoss loss;
    auto input = torch::tensor({2., 4., 1., 3.}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({-1., 1., 1., -1.}, torch::kFloat);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor({1.3767317}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, MultiLabelSoftMarginLossDefaultOptions) {
    MultiLabelSoftMarginLoss loss;
    auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor({0.7608436}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, SoftMarginLossNoReduction) {
    SoftMarginLoss loss(torch::kNone);
    auto input = torch::tensor({2., 4., 1., 3.}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({-1., 1., 1., -1.}, torch::kFloat);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor({2.1269281, 0.01814993, 0.3132617, 3.0485873}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, MultiLabelSoftMarginLossWeightedNoReduction) {
    auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
    auto weight = torch::tensor({0.1, 0.6, 0.4, 0.8}, torch::kFloat);
    auto options = MultiLabelSoftMarginLossOptions().reduction(torch::kNone).weight(weight);
    MultiLabelSoftMarginLoss loss = MultiLabelSoftMarginLoss(options);
    auto output = loss->forward(input, target);
    auto expected = torch::tensor({0.4876902, 0.3321295}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input.sizes(), input.grad().sizes());
  }
  
  TEST_F(ModulesTest, PairwiseDistance) {
    PairwiseDistance dist(PairwiseDistanceOptions().p(1));
    auto input1 = torch::tensor({{1, 2, 3}, {4, 5, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto input2 = torch::tensor({{1, 8, 3}, {2, 1, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
    auto output = dist->forward(input1, input2);
    auto expected = torch::tensor({6, 6}, torch::kFloat);
    auto s = output.sum();
    s.backward();
  
    ASSERT_TRUE(output.allclose(expected));
    ASSERT_EQ(input1.sizes(), input1.grad().sizes());
  }
  
  TEST_F(ModulesTest, ELU) {
    const auto size = 3;
    for (const auto alpha : {0.0, 0.42, 1.0, 4.2, 42.42}) {
      for (const auto inplace : {false, true}) {
        ELU model {ELUOptions().alpha(alpha).inplace(inplace)};
        auto x = torch::linspace(-10.0, 10.0, size * size * size);
        x.resize_({size, size, size});
        if (!inplace) {
          x.requires_grad_(true);
        }
        auto x_orig = x.clone();
        auto y = model(x);
        torch::Tensor s = y.sum();
  
        ASSERT_EQ(s.ndimension(), 0);
  
        ASSERT_EQ(y.ndimension(), 3);
        ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
        auto y_exp = torch::max(torch::zeros_like(x_orig), x_orig) +
                     torch::min(torch::zeros_like(x_orig), alpha * (torch::exp(x_orig) - 1.0));
        ASSERT_TRUE(torch::allclose(y, y_exp));
        if (inplace) {
          ASSERT_TRUE(torch::allclose(x, y_exp));
        } else {
          s.backward();
        }
      }
    }
  }
  
  TEST_F(ModulesTest, SELU) {
    for (const auto inplace : {false, true}) {
      SELU model(inplace);
      auto input = torch::randn({5, 5});
      if (!inplace) {
        input.requires_grad_(true);
      }
      auto input_orig = input.clone();
      auto output = model->forward(input);
      const double scale = 1.0507009873554804934193349852946;
      const double alpha = 1.6732632423543772848170429916717;
      auto zero = torch::zeros_like(input);
      auto expected = scale *
          (torch::max(zero, input_orig) +
           torch::min(zero, alpha * (torch::exp(input_orig) - 1)));
      auto s = output.sum();
  
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_TRUE(output.allclose(expected));
      if (inplace) {
        ASSERT_TRUE(input.allclose(expected));
      } else {
        s.backward();
      }
    }
  }
  
  TEST_F(ModulesTest, Hardshrink) {
    const auto size = 3;
    for (const auto lambda : {-4.2, -1.0, -0.42, 0.0, 0.42, 1.0, 4.2, 42.42}) {
      Hardshrink model {HardshrinkOptions().lambda(lambda)};
      auto x = torch::linspace(-10.0, 10.0, size * size * size);
      x.resize_({size, size, size}).set_requires_grad(true);
      auto y = model(x);
      torch::Tensor s = y.sum();
  
      s.backward();
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_EQ(y.ndimension(), 3);
      ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
      auto y_exp = (x.abs() > lambda) * x;
      ASSERT_TRUE(torch::allclose(y, y_exp));
    }
  }
  
  TEST_F(ModulesTest, Hardtanh) {
    const auto size = 3;
    for (const auto min_val : {-4.2, -1.0, -0.42, 0.0}) {
      for (const auto max_val : {0.42, 1.0, 4.2}) {
        for (const auto inplace : {false, true}) {
          Hardtanh model {HardtanhOptions().min_val(min_val).max_val(max_val).inplace(inplace)};
          auto x = torch::linspace(-10.0, 10.0, size * size * size);
          x.resize_({size, size, size});
          if (!inplace) {
            x.requires_grad_(true);
          }
          auto x_orig = x.clone();
          auto y = model(x);
          torch::Tensor s = y.sum();
  
          ASSERT_EQ(s.ndimension(), 0);
          ASSERT_EQ(y.ndimension(), 3);
          ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
          auto y_exp = (x_orig < min_val) * min_val +
                       ((x_orig >= min_val) * (x_orig <= max_val)) * x_orig +
                       (x_orig > max_val) * max_val;
          ASSERT_TRUE(torch::allclose(y, y_exp));
          if (inplace) {
            ASSERT_TRUE(torch::allclose(x, y_exp));
          } else {
            s.backward();
          }
        }
      }
    }
  }
  
  TEST_F(ModulesTest, HardtanhMinValGEMaxVal) {
    ASSERT_THROWS_WITH(Hardtanh{HardtanhOptions().min_val(0.42).max_val(0.42)},
                       "max_val must be greater than min_val");
    ASSERT_THROWS_WITH(Hardtanh{HardtanhOptions().min_val(0.42).max_val(-0.42)},
                       "max_val must be greater than min_val");
  
    Hardtanh ht {HardtanhOptions().min_val(-0.42).max_val(0.42)};
    ht->options.min_val(0.42);
    ASSERT_THROWS_WITH(ht->reset(), "max_val must be greater than min_val");
    ht->options.max_val(-0.42);
    ASSERT_THROWS_WITH(ht->reset(), "max_val must be greater than min_val");
  }
  
  TEST_F(ModulesTest, LeakyReLU) {
    const auto size = 3;
    for (const auto inplace : {false, true}) {
      for (const auto negative_slope : {0.0, 0.42, 1.0}) {
        LeakyReLU model {LeakyReLUOptions().negative_slope(negative_slope).inplace(inplace)};
        auto x = torch::linspace(-10.0, 10.0, size * size * size);
        x.resize_({size, size, size});
        if (!inplace) {
          x.requires_grad_(true);
        }
        auto x_orig = x.clone();
        auto y = model(x);
        torch::Tensor s = y.sum();
  
        ASSERT_EQ(s.ndimension(), 0);
        ASSERT_EQ(y.ndimension(), 3);
        ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
        auto y_exp = (x_orig < 0) * x_orig * negative_slope + (x_orig >= 0) * x_orig;
        ASSERT_TRUE(torch::allclose(y, y_exp));
        if (inplace) {
          ASSERT_TRUE(torch::allclose(x, y_exp));
        } else {
          s.backward();
        }
      }
    }
  }
  
  TEST_F(ModulesTest, LogSigmoid) {
    const auto size = 3;
    LogSigmoid model;
    auto x = torch::linspace(-10.0, 10.0, size * size * size);
    x.resize_({size, size, size}).set_requires_grad(true);
    auto y = model(x);
    torch::Tensor s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), 3);
    ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
    auto y_exp = torch::log(torch::ones_like(x)/(torch::ones_like(x) + torch::exp(torch::neg(x))));
    ASSERT_TRUE(torch::allclose(y, y_exp, 1e-4, 1e-7));
  }
  
  TEST_F(ModulesTest, Softmax) {
    Softmax m(/*dim=*/1);
    auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
    auto output = m(input);
    auto sum = torch::sum(torch::exp(input), 1);
  
    for (int i = 0; i < 2; i++) {
      auto expected = torch::exp(input[i]) / sum[i];
      ASSERT_TRUE(torch::allclose(output[i], expected));
    }
  }
  
  TEST_F(ModulesTest, Softmin) {
    Softmin m(/*dim=*/1);
    auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
    auto output = m(input);
    auto sum = torch::sum(torch::exp(-input), 1);
  
    for (int i = 0; i < 2; i++) {
      auto expected = torch::exp(-input[i]) / sum[i];
      ASSERT_TRUE(torch::allclose(output[i], expected));
    }
  }
  
  TEST_F(ModulesTest, LogSoftmax) {
    LogSoftmax m(/*dim=*/1);
    auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
    auto output = m(input);
    auto sum = torch::sum(torch::exp(input), 1);
  
    for (int i = 0; i < 2; i++) {
      auto expected = torch::log(torch::exp(input[i]) / sum[i]);
      ASSERT_TRUE(torch::allclose(output[i], expected));
    }
  }
  
  TEST_F(ModulesTest, AdaptiveLogSoftmaxWithLoss) {
    {
      // log_probs actually returns log_proba
      AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 4, {2}).div_value(2.));
      auto x = torch::randn({4, 8});
      auto logprob_out = asfm->log_prob(x);
      ASSERT_TRUE(torch::allclose(torch::exp(logprob_out).data().sum(1), torch::ones(4)));
    }
    {
      // test predict
      AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 10, {4, 8}).div_value(2.).head_bias(true));
      auto x = torch::randn({64, 8});
      auto logprob_out = asfm->log_prob(x);
      auto predict_out = asfm->predict(x);
      ASSERT_TRUE(torch::allclose(predict_out, logprob_out.argmax(1)));
    }
    {
      // cluster sizes
      AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(16, 20, {4, 10, 15}).div_value(2.));
      auto x = torch::arange(100, 132, torch::kFloat).reshape({2, 16});
      auto y = torch::tensor({0, 17}, torch::kLong);
      auto asm_out = asfm(x, y);
      ASSERT_EQ(asm_out.output.sizes(), std::vector<int64_t>({2}));
    }
    {
      // forward returns the same thing as log_probs
      AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 4, {2}).div_value(2.));
      auto x = torch::randn({4, 8});
      auto logprob_out = asfm->log_prob(x);
      NLLLoss nll_loss;
  
      for (int64_t v = 0; v < 4; ++v) {
        auto y = torch::full({4}, v, torch::kLong);
        auto asm_out = asfm(x, y);
        auto out = asm_out.output;
        auto loss = torch::tensor(asm_out.loss, torch::kFloat);
        auto expected = nll_loss->forward(logprob_out, y);
  
        ASSERT_TRUE(torch::allclose(loss, expected));
        ASSERT_TRUE(torch::allclose(out, logprob_out.gather(1, y.unsqueeze(1)).squeeze()));
      }
    }
  }
  
  TEST_F(ModulesTest, Softmax2d) {
    Softmax2d m;
    auto input = torch::arange(24, torch::kFloat).reshape({1, 2, 3, 4});
    auto output = m(input);
    auto sum = torch::sum(torch::exp(input), 1);
  
    for (int i = 0; i < 1; i++) {
      for (int j = 0; j < 2; j++) {
        for (int k = 0; k < 3; k++) {
          for (int l = 0; l < 4; l++) {
            auto expected = torch::exp(input[i][j][k][l]) / sum[i][k][l];
            ASSERT_TRUE(torch::allclose(output[i][j][k][l], expected));
          }
        }
      }
    }
  }
  
  TEST_F(ModulesTest, PReLU) {
    const auto num_parameters = 42;
    const auto init = 0.42;
  
    PReLU model {PReLUOptions().num_parameters(num_parameters).init(init)};
  
    ASSERT_EQ(model->weight.sizes(), std::vector<int64_t>({num_parameters}));
    ASSERT_TRUE(torch::allclose(model->weight,
                torch::full(num_parameters, init)));
  
    const auto x = torch::rand({100, num_parameters}) * 200 - 100;
    const auto y = model(x);
    const auto s = y.sum();
  
    s.backward();
    ASSERT_EQ(s.ndimension(), 0);
  
    ASSERT_EQ(y.ndimension(), x.ndimension());
    ASSERT_EQ(y.sizes(), x.sizes());
    const auto y_exp = (x < 0) * model->weight * x  + (x >= 0) * x;
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, ReLU) {
    for (const auto inplace : {false, true}) {
      const auto size = 3;
      ReLU model(inplace);
      auto x = torch::linspace(-10.0, 10.0, size * size * size);
      x.resize_({size, size, size});
      if (!inplace) {
        x.requires_grad_(true);
      }
      auto x_orig = x.clone();
      auto y = model(x);
      torch::Tensor s = y.sum();
  
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_EQ(y.ndimension(), 3);
      ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
      auto y_exp = (x_orig < 0) * 0 + (x_orig >= 0) * x_orig;
      ASSERT_TRUE(torch::allclose(y, y_exp));
      if (inplace) {
        ASSERT_TRUE(torch::allclose(x, y_exp));
      } else {
        s.backward();
      }
    }
  }
  
  TEST_F(ModulesTest, ReLU6) {
    for (const auto inplace : {false, true}) {
      const auto size = 3;
      ReLU6 model(inplace);
      auto x = torch::linspace(-10.0, 10.0, size * size * size);
      x.resize_({size, size, size});
      if (!inplace) {
        x.requires_grad_(true);
      }
      auto x_orig = x.clone();
      auto y = model(x);
      torch::Tensor s = y.sum();
  
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_EQ(y.ndimension(), 3);
      ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
      auto y_exp = (x_orig < 0) * 0 + ((x_orig >= 0) * (x_orig <= 6)) * x_orig + (x_orig > 6) * 6;
      ASSERT_TRUE(torch::allclose(y, y_exp));
      if (inplace) {
        ASSERT_TRUE(torch::allclose(x, y_exp));
      } else {
        s.backward();
      }
    }
  }
  
  TEST_F(ModulesTest, RReLU) {
    const auto size = 3;
    for (const auto lower : {0.01, 0.1, 0.2}) {
      for (const auto upper : {0.3, 0.4, 0.5}) {
        for (const auto inplace : {false, true}) {
          RReLU model {RReLUOptions().lower(lower).upper(upper).inplace(inplace)};
          auto x = torch::linspace(-10.0, 10.0, size * size * size);
          x.resize_({size, size, size});
          if (!inplace) {
            x.requires_grad_(true);
          }
          auto x_orig = x.clone();
          auto y = model(x);
          torch::Tensor s = y.sum();
  
          ASSERT_EQ(s.ndimension(), 0);
          ASSERT_EQ(y.ndimension(), 3);
          ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
          auto z = ((x_orig >= 0) * (x_orig == y) +
            (x_orig < 0) * (y >= x_orig * upper) * (y <= lower * x_orig)) * 1.0;
          ASSERT_TRUE(torch::allclose(z, torch::ones_like(z)));
          if (inplace) {
            ASSERT_TRUE(torch::allclose(x, y));
          } else {
            s.backward();
          }
        }
      }
    }
  }
  
  TEST_F(ModulesTest, CELU) {
    const auto size = 3;
    for (const auto inplace : {false, true}) {
      for (const auto alpha : {0.42, 1.0, 4.2, 42.42}) {
        CELU model {CELUOptions().alpha(alpha).inplace(inplace)};
        auto x = torch::linspace(-10.0, 10.0, size * size * size);
        x.resize_({size, size, size});
        if (!inplace) {
          x.requires_grad_(true);
        }
        auto x_orig = x.clone();
        auto y = model(x);
        torch::Tensor s = y.sum();
  
        ASSERT_EQ(s.ndimension(), 0);
        ASSERT_EQ(y.ndimension(), 3);
        ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
        auto y_exp = torch::max(torch::zeros_like(x_orig), x_orig) +
            torch::min(torch::zeros_like(x_orig), alpha * (torch::exp(x_orig / alpha) - 1.0));
        ASSERT_TRUE(torch::allclose(y, y_exp));
        if (inplace) {
          ASSERT_TRUE(torch::allclose(x, y_exp));
        } else {
          s.backward();
        }
      }
    }
  }
  
  TEST_F(ModulesTest, GLU) {
    int64_t dim = 1;
    GLU model(dim);
    auto input = torch::randn({4, 2}, torch::requires_grad());
    auto output = model->forward(input);
    auto input_size = input.sizes()[dim] / 2;
    auto first_half = input.narrow(dim, 0, input_size);
    auto second_half = input.narrow(dim, input_size, input_size);
    auto expected = first_half * torch::sigmoid(second_half);
    auto s = output.sum();
    s.backward();
  
    ASSERT_EQ(s.ndimension(), 0);
    ASSERT_TRUE(output.allclose(expected));
  
    GLU model_default_options;
    ASSERT_TRUE(model_default_options->forward(input).allclose(expected));
  }
  
  TEST_F(ModulesTest, GELU) {
    GELU model;
    const auto x = torch::linspace(-3.0, 3.0, 100);
    const auto y_exp = x * 0.5 * (1.0 + torch::erf(x / std::sqrt(2.0)));
    const auto y = model(x);
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, Mish) {
    Mish model;
    auto x = torch::randn(100) * 10;
    auto y_exp = x * x.exp().log1p().tanh();
    auto y = model(x);
  
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, Sigmoid) {
    Sigmoid model;
    auto x = torch::randn(100) * 10;
    auto y_exp = 1 / (1 + torch::exp(-x));
    auto y = model(x);
  
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, PixelShuffle) {
    PixelShuffle module(/*upscale_factor=*/2);
    auto x = torch::tensor(
      {{{{-17, 19}, {-1, 2}},
        {{7, 14}, {-3, 1}},
        {{0, -2}, {-12, 14}},
        {{-15, 0}, {-3, 9}}}}, torch::kFloat);
    auto y_exp = torch::tensor(
      {{{{-17, 7, 19, 14},
         {0, -15, -2, 0},
         {-1, -3, 2, 1},
         {-12, -3, 14, 9}}}}, torch::kFloat);
    auto y = module(x);
  
    ASSERT_EQ(y.ndimension(), 4);
    ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 4, 4}));
    ASSERT_TRUE(y.allclose(y_exp));
  }
  
  TEST_F(ModulesTest, PixelUnshuffle) {
    PixelUnshuffle module(/*downscale_factor=*/2);
    auto x = torch::tensor(
        {{{{-17, 7, 19, 14}, {0, -15, -2, 0}, {-1, -3, 2, 1}, {-12, -3, 14, 9}}}},
        torch::kFloat);
    auto y_exp = torch::tensor(
        {{{{-17, 19}, {-1, 2}},
          {{7, 14}, {-3, 1}},
          {{0, -2}, {-12, 14}},
          {{-15, 0}, {-3, 9}}}},
        torch::kFloat);
    auto y = module(x);
  
    ASSERT_EQ(y.ndimension(), 4);
    ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 4, 2, 2}));
    ASSERT_TRUE(y.allclose(y_exp));
  }
  
  TEST_F(ModulesTest, Softplus) {
    const auto size = 3;
    for (const auto beta : {0.5, 1.0, 2.0}) {
      for (const auto threshold : {1.0, 3.0, 5.0}) {
        Softplus model {SoftplusOptions().beta(beta).threshold(threshold)};
        auto x = torch::linspace(-3.0, 3.0, 61);
        x.resize_({size, size, size});
        auto y_exp =
          (x <= threshold) * torch::log(1 + torch::exp(x * beta)) / beta +
          (x > threshold) * x;
        auto y = model(x);
  
        ASSERT_EQ(y.ndimension(), 3);
        ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
        ASSERT_TRUE(torch::allclose(y, y_exp));
      }
    }
  }
  
  TEST_F(ModulesTest, Softshrink) {
    const auto size = 3;
    for (const auto lambda : {0.0, 0.42, 1.0, 4.2, 42.42}) {
      Softshrink model {/*lambda=*/lambda};
      auto x = torch::linspace(-10.0, 10.0, size * size * size);
      x.resize_({size, size, size}).set_requires_grad(true);
      auto y = model(x);
      torch::Tensor s = y.sum();
  
      s.backward();
      ASSERT_EQ(s.ndimension(), 0);
  
      ASSERT_EQ(y.ndimension(), 3);
      ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
      auto y_exp = (x < -lambda) * (x + lambda) + (x > lambda) * (x - lambda);
      ASSERT_TRUE(torch::allclose(y, y_exp));
    }
  }
  
  TEST_F(ModulesTest, Softsign) {
    Softsign model;
    auto x = torch::randn(100) * 10;
    auto y_exp = x / (1 + x.abs());
    auto y = model(x);
  
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, Tanh) {
    Tanh model;
    auto x = torch::randn(100) * 10;
    auto y_exp = (x.exp() - (-x).exp()) / (x.exp() + (-x).exp());
    auto y = model(x);
  
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, Tanhshrink) {
    Tanhshrink model;
    auto x = torch::randn(100) * 10;
    auto y_exp = x - x.tanh();
    auto y = model(x);
  
    ASSERT_TRUE(torch::allclose(y, y_exp));
  }
  
  TEST_F(ModulesTest, Threshold) {
    const auto size = 3;
    for (const auto threshold : {0.5, 1.0, 2.0}) {
      for (const auto value : {0.5, 1.0, 2.0}) {
        for (const auto inplace : {false, true}) {
          Threshold model {ThresholdOptions(threshold, value).inplace(inplace)};
          auto x = torch::linspace(-3.0, 3.0, 61);
          x.resize_({size, size, size});
          auto x_orig = x.clone();
          auto y_exp = (x_orig <= threshold) * value + (x_orig > threshold) * x_orig;
          auto y = model(x);
  
          ASSERT_EQ(y.ndimension(), 3);
          ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
          ASSERT_TRUE(torch::allclose(y, y_exp));
          if (inplace) {
            ASSERT_TRUE(torch::allclose(x, y_exp));
          }
        }
      }
    }
  }
  
  TEST_F(ModulesTest, Upsampling1D) {
    {
      Upsample model(UpsampleOptions()
                         .size(std::vector<int64_t>({4}))
                         .mode(torch::kNearest));
      auto input = torch::ones({1, 1, 2}, torch::requires_grad());
      auto output = model->forward(input);
      auto expected = torch::ones({1, 1, 4});
      auto s = output.sum();
      s.backward();
  
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      for (const auto align_corners : {true, false}) {
        // test float scale factor up & down sampling
        for (const auto scale_factor : {0.5, 1.5, 2.0}) {
          Upsample model(UpsampleOptions()
                             .scale_factor(std::vector<double>({scale_factor}))
                             .mode(torch::kLinear)
                             .align_corners(align_corners));
          auto input = torch::ones({1, 1, 2}, torch::requires_grad());
          auto output = model->forward(input);
          auto expected_size =
              static_cast<int64_t>(std::floor(input.size(-1) * scale_factor));
          auto expected = torch::ones({1, 1, expected_size});
          auto s = output.sum();
          s.backward();
  
          ASSERT_EQ(s.ndimension(), 0);
          ASSERT_TRUE(output.allclose(expected));
        }
      }
    }
    {
      // linear (1D) upsampling spatial invariance
      Upsample model(UpsampleOptions()
                         .scale_factor(std::vector<double>({3}))
                         .mode(torch::kLinear)
                         .align_corners(false));
      auto input = torch::zeros({1, 1, 9});
      input.narrow(2, 0, 4).normal_();
      auto output = model->forward(input);
      auto expected = model->forward(input.narrow(2, 0, 5));
  
      ASSERT_TRUE(torch::allclose(output.narrow(2, 0, 15), expected));
    }
  }
  
  TEST_F(ModulesTest, Upsampling2D) {
    {
      Upsample model(UpsampleOptions()
                         .size(std::vector<int64_t>({4, 4}))
                         .mode(torch::kNearest));
      auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
      auto output = model->forward(input);
      auto expected = torch::ones({1, 1, 4, 4});
      auto s = output.sum();
      s.backward();
  
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      for (const auto align_corners : {true, false}) {
        // test float scale factor up & down sampling
        for (const auto scale_factor : {0.5, 1.5, 2.0}) {
          Upsample model(UpsampleOptions()
                             .scale_factor(std::vector<double>({scale_factor, scale_factor}))
                             .mode(torch::kBilinear)
                             .align_corners(align_corners));
          auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
          auto output = model->forward(input);
          auto expected_size =
              static_cast<int64_t>(std::floor(input.size(-1) * scale_factor));
          auto expected = torch::ones({1, 1, expected_size, expected_size});
          auto s = output.sum();
          s.backward();
  
          ASSERT_EQ(s.ndimension(), 0);
          ASSERT_TRUE(output.allclose(expected));
        }
      }
    }
    {
      for (const auto align_corners : {true, false}) {
        // test float scale factor up & down sampling
        for (const auto scale_factor : {0.5, 1.5, 2.0}) {
          Upsample model(UpsampleOptions()
                             .scale_factor(std::vector<double>({scale_factor, scale_factor}))
                             .mode(torch::kBicubic)
                             .align_corners(align_corners));
          auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
          auto output = model->forward(input);
          auto expected_size =
              static_cast<int64_t>(std::floor(input.size(-1) * scale_factor));
          auto expected = torch::ones({1, 1, expected_size, expected_size});
          auto s = output.sum();
          s.backward();
  
          ASSERT_EQ(s.ndimension(), 0);
          ASSERT_TRUE(output.allclose(expected));
        }
      }
    }
  }
  
  TEST_F(ModulesTest, Upsampling3D) {
    {
      Upsample model(UpsampleOptions()
                         .size(std::vector<int64_t>({4, 4, 4}))
                         .mode(torch::kNearest));
      auto input = torch::ones({1, 1, 2, 2, 2}, torch::requires_grad());
      auto output = model->forward(input);
      auto expected = torch::ones({1, 1, 4, 4, 4});
      auto s = output.sum();
      s.backward();
  
      ASSERT_EQ(s.ndimension(), 0);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      for (const auto align_corners : {true, false}) {
        // test float scale factor up & down sampling
        for (const auto scale_factor : {0.5, 1.5, 2.0}) {
          Upsample model(
              UpsampleOptions()
                  .scale_factor(std::vector<double>({scale_factor, scale_factor, scale_factor}))
                  .mode(torch::kTrilinear)
                  .align_corners(align_corners));
          auto input = torch::ones({1, 1, 2, 2, 2}, torch::requires_grad());
          auto output = model->forward(input);
          auto expected_size =
              static_cast<int64_t>(std::floor(input.size(-1) * scale_factor));
          auto expected =
              torch::ones({1, 1, expected_size, expected_size, expected_size});
          auto s = output.sum();
          s.backward();
  
          ASSERT_EQ(s.ndimension(), 0);
          ASSERT_TRUE(output.allclose(expected));
        }
      }
    }
  }
  
  TEST_F(ModulesTest, CTCLoss) {
    CTCLoss loss {CTCLossOptions().reduction(torch::kNone)};
    const auto target_lengths = torch::tensor({0, 0, 0});
    const auto input_lengths = torch::tensor({50, 50, 50});
    const auto targets =
      torch::randint(1, 15, at::IntArrayRef({0}), torch::kLong);
    const auto log_probs =
      torch::randn({50, 3, 15}, torch::kDouble).log_softmax(2);
    const auto output =
      loss->forward(log_probs, targets, input_lengths, target_lengths);
    ASSERT_TRUE(output.ge(0).all().item<bool>());
    ASSERT_TRUE(torch::allclose(
      -log_probs.sum(0).slice(1, 0, 1).view_as(output), output));
  }
  
  TEST_F(ModulesTest, PoissonNLLLoss) {
    const auto input = torch::tensor({0.5, 1.5, 2.5});
    const auto target = torch::tensor({1., 2., 3.});
    const auto component_wise_loss = torch::exp(input) - target * input;
    {
      PoissonNLLLoss loss {PoissonNLLLossOptions().reduction(torch::kNone)};
      ASSERT_TRUE(torch::allclose(
        component_wise_loss,
        loss->forward(input, target)
      ));
    }
    {
      PoissonNLLLoss loss {PoissonNLLLossOptions().reduction(torch::kSum)};
      ASSERT_TRUE(torch::allclose(
        torch::sum(component_wise_loss),
        loss->forward(input, target)
      ));
    }
    {
      PoissonNLLLoss loss {PoissonNLLLossOptions().reduction(torch::kMean)};
      ASSERT_TRUE(torch::allclose(
        torch::mean(component_wise_loss),
        loss->forward(input, target)
      ));
    }
  }
  
  TEST_F(ModulesTest, MarginRankingLoss) {
    {
      MarginRankingLoss loss;
      const auto input1 = torch::randn(15) * 10;
      const auto input2 = torch::randn(15) * 10;
      const auto target = torch::randn(15).sign();
      ASSERT_TRUE(torch::allclose(
        loss->forward(input1, input2, target),
        (-target * (input1 - input2)).clamp(0).mean()
      ));
    }
    {
      MarginRankingLoss loss {MarginRankingLossOptions().margin(0.5).reduction(torch::kSum)};
      const auto input1 = torch::randn(15) * 10;
      const auto input2 = torch::randn(15) * 10;
      const auto target = torch::randn(15).sign();
      const auto margin = 0.5;
      ASSERT_TRUE(torch::allclose(
        loss->forward(input1, input2, target),
        (-target * (input1 - input2) + margin).clamp(0).sum()
      ));
    }
    {
      MarginRankingLoss loss {MarginRankingLossOptions().margin(0.5).reduction(torch::kMean)};
      const auto input1 = torch::randn(15) * 10;
      const auto input2 = torch::randn(15) * 10;
      const auto target = torch::randn(15).sign();
      const auto margin = 0.5;
      ASSERT_TRUE(torch::allclose(
        loss->forward(input1, input2, target),
        (-target * (input1 - input2) + margin).clamp(0).mean()
      ));
    }
  }
  
  TEST_F(ModulesTest, BCEWithLogitsLoss) {
    { // test BCE with logits raises if target and input are different size
      {
        const auto target = torch::rand(5);
        const auto input = torch::rand({5, 1});
        ASSERT_THROWS_WITH(
          BCEWithLogitsLoss()(input, target),
          "must be the same as input size"
        );
      }
      {
        const auto target = torch::rand({5, 1});
        const auto input = torch::rand(5);
        ASSERT_THROWS_WITH(
          BCEWithLogitsLoss()(input, target),
          "must be the same as input size"
        );
      }
    }
    { // test BCE with logits gives same result as sigmoid and bce loss
      auto sigmoid = Sigmoid();
  
      auto target = torch::rand({64, 4});
      auto output = torch::rand({64, 4}) - 0.5;
  
      ASSERT_TRUE(torch::allclose(
        BCEWithLogitsLoss()(output, target),
        BCELoss()(sigmoid(output), target)
      ));
  
      auto weight = torch::rand(4);
      ASSERT_TRUE(torch::allclose(
        BCEWithLogitsLoss(
          BCEWithLogitsLossOptions().weight(weight)
        )(output, target),
        BCELoss(
          BCELossOptions().weight(weight)
        )(sigmoid(output), target)
      ));
  
      target = torch::zeros({4, 1}, torch::kFloat);
      output = torch::empty({4, 1}, torch::kFloat).fill_(-100);
  
      ASSERT_TRUE(torch::allclose(
        BCEWithLogitsLoss()(output, target),
        BCELoss()(sigmoid(output), target)
      ));
  
      ASSERT_TRUE(torch::allclose(
        BCEWithLogitsLoss(
          BCEWithLogitsLossOptions().reduction(torch::kNone)
        )(output, target),
        BCELoss(
          BCELossOptions().reduction(torch::kNone)
        )(sigmoid(output), target)
      ));
  
      weight = torch::rand({1}, torch::kFloat);
      ASSERT_TRUE(torch::allclose(
        BCEWithLogitsLoss(
          BCEWithLogitsLossOptions().weight(weight)
        )(output, target),
        BCELoss(
          BCELossOptions().weight(weight)
        )(sigmoid(output), target)
      ));
    }
    { // test BCE with logits has correct grad at zero
      const auto output = torch::zeros({3, 1}, torch::requires_grad());
      const auto target = torch::zeros({3, 1});
      BCEWithLogitsLoss(BCEWithLogitsLossOptions()
        .reduction(torch::kSum))(output, target).backward();
      const auto expected_grad = torch::empty({3, 1}).fill_(0.5);
      ASSERT_TRUE(torch::allclose(output.grad(), expected_grad));
    }
    { // test BCE with logits broadcasts weights
      const auto target = torch::rand({16, 4});
      const auto output = torch::rand({16, 4}) - 0.5;
  
      auto weight = torch::rand(4);
      auto out1 = BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().weight(weight)
      )(output, target);
  
      weight = weight.expand({16, 4}).contiguous();
      auto out2 = BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().weight(weight)
      )(output, target);
  
      ASSERT_TRUE(torch::allclose(out1, out2));
  
      weight = torch::rand({16, 1});
      out1 = BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().weight(weight)
      )(output, target);
  
      weight = weight.expand({16, 4}).contiguous();
      out2 = BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().weight(weight)
      )(output, target);
  
      ASSERT_TRUE(torch::allclose(out1, out2));
    }
    { // test BCE with logits ones in pos weights are the same as none
      const auto target = torch::rand({64, 4});
      const auto output = torch::rand({64, 4}) - 0.5;
      const auto pos_weight = torch::ones({64, 4});
  
      ASSERT_TRUE(torch::allclose(
        BCEWithLogitsLoss()(output, target),
        BCEWithLogitsLoss(
          BCEWithLogitsLossOptions().pos_weight(pos_weight)
        )(output, target)
      ));
    }
    { // test BCE with logits broadcasts pos weights
      const auto target = torch::rand({64, 4});
      const auto output = torch::rand({64, 4}) - 0.5;
      const auto pos_weight = torch::rand(4);
      const auto out1 = BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().pos_weight(pos_weight)
      )(output, target);
  
      const auto pos_weight1 = pos_weight.expand({1, 4});
      const auto out2 = BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().pos_weight(pos_weight)
      )(output, target);
  
      const auto pos_weight2 = pos_weight.expand({64, 4});
      const auto out3 = BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().pos_weight(pos_weight)
      )(output, target);
  
      ASSERT_TRUE(torch::allclose(out1, out2));
      ASSERT_TRUE(torch::allclose(out1, out3));
    }
    { // test BCE with logits with pos weight has correct grad at zero
      const auto output = torch::zeros({3, 1}, torch::requires_grad());
      const auto target = torch::zeros({3, 1});
      const auto pos_weight = torch::ones({3, 1});
      BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().pos_weight(pos_weight).reduction(torch::kSum)
      )(output, target).backward();
      const auto expected_grad = torch::empty({3, 1}).fill_(0.5);
      // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
      const auto grad = output.grad();
      ASSERT_TRUE(torch::allclose(grad, expected_grad));
    }
    { // test BCE with logits stability
      const auto output = torch::tensor({0., -120.});
      const auto target = torch::tensor({0., 1.});
      const auto pos_weight = torch::tensor({1., 1.});
  
      const auto out1 = BCEWithLogitsLoss()(output, target);
      ASSERT_TRUE(torch::isfinite(out1).all().item<bool>());
  
      const auto out2 = BCEWithLogitsLoss(
        BCEWithLogitsLossOptions().pos_weight(pos_weight)
      )(output, target);
      ASSERT_TRUE(torch::isfinite(out2).all().item<bool>());
    }
  }
  
  namespace detail {
  
    namespace F = torch::nn::functional;
  
    torch::Tensor _batchmatmul(const torch::Tensor& a, const torch::Tensor& b) {
      TORCH_INTERNAL_ASSERT(a.size(0) == b.size(0));
      TORCH_INTERNAL_ASSERT(a.size(1) == b.size(1));
      auto retval = torch::zeros({a.size(0), a.size(1), a.size(2), b.size(3)}, torch::kFloat32);
      for (int i = 0; i < a.size(0); i++) {
        for (int j = 0; j < a.size(1); j++) {
          retval[i][j] = torch::matmul(a[i][j], b[i][j]);
        }
      }
      return retval;
    }
  
    torch::Tensor _softmax(const torch::Tensor& x) {
      auto output = torch::zeros(x.sizes());
      for (int i = 0; i < x.size(0); i++) {
        for (int j = 0; j < x.size(1); j++) {
          for (int k = 0; k < x.size(2); k++) {
            const auto& x_curr = x[i][j][k];
            const auto e_x = torch::exp(x_curr - torch::max(x_curr));
            output[i][j][k] = e_x / torch::sum(e_x);
          }
        }
      }
      return output;
    }
  
    std::tuple<torch::Tensor, torch::Tensor> _scaled_dot_attn_ref(
      const torch::Tensor& Q, const torch::Tensor& K, const torch::Tensor& V,
      at::IntArrayRef dims, const torch::Tensor& unseen_mask = {},
      const torch::Tensor& key_padding_mask = {}) {
      auto QKT = _batchmatmul(
        Q,
        K.permute({0, 1, 3, 2}) / std::sqrt(dims[3])
      );
      const auto b1 = QKT.size(0);
      const auto b2 = QKT.size(1);
      const auto s1 = QKT.size(2);
      const auto s2 = QKT.size(3);
      if (unseen_mask.defined() || key_padding_mask.defined()) {
        for (int i = 0; i < b1; i++) {
          for (int j = 0; j < b2; j++) {
            for (int m = 0; m < s1; m++) {
              for (int n = 0; n < s2; n++) {
                if (unseen_mask.defined() && unseen_mask[m][n].item<double>() == 0) {
                  QKT[i][j][m][n] = -std::numeric_limits<double>::infinity();
                }
                if (key_padding_mask.defined() && key_padding_mask[i][n].item<double>() != 0) {
                  QKT[i][j][m][n] = -std::numeric_limits<double>::infinity();
                }
              }
            }
          }
        }
      }
      auto reference = _softmax(QKT);
      auto ref_attn_weight = reference;
      // NOLINTNEXTLINE(bugprone-argument-comment)
      ref_attn_weight = torch::sum(ref_attn_weight, /*axis=*/1) / b2;
      reference = _batchmatmul(reference, V);
      return std::tie(reference, ref_attn_weight);
    }
  
    torch::Tensor _split_heads_ref(const torch::Tensor& X, at::IntArrayRef dims, int nheads, int d_head) {
      auto X_split = X.reshape({dims[0], dims[1], nheads, d_head});
      auto X_split_transposed = X_split.permute({0, 2, 1, 3});
      return X_split_transposed.reshape({dims[0], nheads, dims[1], d_head});
    }
  
    torch::Tensor _combine_heads_ref(const torch::Tensor& X, at::IntArrayRef dims, int nheads, int d_head) {
      auto X_transposed = X.permute({0, 2, 1, 3});
      auto reference = X_transposed.reshape({dims[0], dims[1], nheads * d_head});
      return reference;
    }
  
    torch::Tensor _fc(torch::Tensor X, torch::Tensor X_weight, torch::Tensor X_bias) {
      // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
      auto X_fc_b = X_bias;
      // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
      auto X_fc_w = X_weight;
      return torch::matmul(X, torch::t(X_fc_w)) + X_fc_b;
    }
  
    void _multihead_attn_test_helper(bool add_key_padding_mask = false,
      bool add_bias_kv = false, bool add_zero_attn = false,
      bool saved_kv = false, bool same_embed_dim = false) {
      std::random_device device;
      std::mt19937 generator(device());
      std::uniform_int_distribution<int> d_2_10(2, 10);
      std::uniform_int_distribution<int> d_3_10(3, 10);
      bool registration_checked = false;
      for (int i = 0; i < 100; i++) {
        const auto batch_sz = d_2_10(generator);
        const auto seq_len = d_2_10(generator);
        const auto d_head = d_3_10(generator);
        const auto nheads = d_3_10(generator);
        const auto d_model = d_head * nheads;
        // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
        int kv_dim;
        if (same_embed_dim) {
          kv_dim = d_model;
        } else {
          std::uniform_int_distribution<int> d(5, 20);
          kv_dim = d(generator);
          while (kv_dim == d_model) {
            kv_dim = d(generator);
          }
        }
        std::vector<int64_t> dims {batch_sz, seq_len, kv_dim};
        torch::Tensor saved_k;
        torch::Tensor saved_k_tensor;
        torch::Tensor saved_v;
        torch::Tensor saved_v_tensor;
        if (saved_kv) {
          saved_k = torch::rand({batch_sz * nheads, seq_len, d_head});
          saved_k_tensor = saved_k;
          saved_v = torch::rand({batch_sz * nheads, seq_len, d_head});
          saved_v_tensor = saved_v;
        }
        torch::Tensor key_padding_mask;
        torch::Tensor key_padding_mask_tensor;
        if (add_key_padding_mask) {
          const auto seq_mask = torch::randint(0, 2, {1, seq_len});
          key_padding_mask = seq_mask.repeat({batch_sz, 1}) == 1;
          key_padding_mask_tensor = key_padding_mask;
        }
        const auto decoder_state = torch::rand({batch_sz, d_model});
        const torch::Tensor K = torch::rand(dims);
        // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
        const torch::Tensor V = K;
        const torch::Tensor Q = decoder_state.clone().resize_({batch_sz, 1, d_model});
        auto attn_mask = torch::randint(0, 2, {1, seq_len});
        const torch::Tensor attn_mask_tensor = attn_mask.clone();
        attn_mask_tensor.masked_fill_(attn_mask_tensor == 0, -std::numeric_limits<double>::infinity());
        attn_mask_tensor.masked_fill_(attn_mask_tensor > 0, double(0.0));
  
        // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
        const torch::Tensor decoder_state_tensor = decoder_state;
        const torch::Tensor source_hid_tensor = K.transpose(0, 1);
  
        const auto options = MultiheadAttentionOptions(d_model, nheads)
            .add_bias_kv(add_bias_kv)
            .add_zero_attn(add_zero_attn)
            .kdim(kv_dim)
            .vdim(kv_dim);
        const auto multihead_attn_module = MultiheadAttention(options);
  
        if (!registration_checked) {
          // make sure parameters are all registered correctly
          auto named_parameters = multihead_attn_module->named_parameters();
          if (same_embed_dim) {
            ASSERT_TRUE(named_parameters.contains("in_proj_weight"));
          }
          else {
            ASSERT_TRUE(named_parameters.contains("q_proj_weight"));
            ASSERT_TRUE(named_parameters.contains("k_proj_weight"));
            ASSERT_TRUE(named_parameters.contains("v_proj_weight"));
          }
          if (add_bias_kv) {
            ASSERT_TRUE(named_parameters.contains("bias_k"));
            ASSERT_TRUE(named_parameters.contains("bias_v"));
          }
          // make sure sub modules are all registered correctly
          auto submodules = multihead_attn_module->named_children();
          ASSERT_TRUE(submodules.contains("out_proj"));
          registration_checked = true;
        }
  
        torch::Tensor bias_k;
        torch::Tensor bias_v;
        if (add_bias_kv) {
          bias_k = multihead_attn_module->bias_k.detach();
          bias_v = multihead_attn_module->bias_v.detach();
        } else {
          bias_k = {};
          bias_v = {};
        }
  
        torch::Tensor _Q = decoder_state_tensor.unsqueeze(1).transpose(0, 1);
        // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
        torch::Tensor _V = source_hid_tensor;
        // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
        torch::Tensor _K = source_hid_tensor;
  
        torch::Tensor result;
        torch::Tensor result_weight;
        if (multihead_attn_module->_qkv_same_embed_dim) {
          std::tie(result, result_weight) = F::multi_head_attention_forward(
            _Q, _K, _V,
            F::MultiheadAttentionForwardFuncOptions(
              /*embed_dim_to_check=*/d_model,
              /*num_heads=*/nheads,
              /*in_proj_weight=*/multihead_attn_module->in_proj_weight,
              /*in_proj_bias=*/multihead_attn_module->in_proj_bias,
              /*bias_k=*/multihead_attn_module->bias_k,
              /*bias_v=*/multihead_attn_module->bias_v,
              /*add_zero_attn=*/multihead_attn_module->options.add_zero_attn(),
              /*dropout_p=*/multihead_attn_module->options.dropout(),
              /*out_proj_weight=*/multihead_attn_module->out_proj->weight,
              /*out_proj_bias=*/multihead_attn_module->out_proj->bias
            )
            .training(multihead_attn_module->is_training())
            .key_padding_mask(key_padding_mask_tensor)
            .need_weights(true)
            .attn_mask(attn_mask_tensor)
            .static_k(saved_k_tensor)
            .static_v(saved_v_tensor)
          );
        } else {
          std::tie(result, result_weight) = F::multi_head_attention_forward(
            _Q, _K, _V,
            F::MultiheadAttentionForwardFuncOptions(
              /*embed_dim_to_check=*/d_model,
              /*num_heads=*/nheads,
              /*in_proj_weight=*/{},
              /*in_proj_bias=*/multihead_attn_module->in_proj_bias,
              /*bias_k=*/multihead_attn_module->bias_k,
              /*bias_v=*/multihead_attn_module->bias_v,
              /*add_zero_attn=*/multihead_attn_module->options.add_zero_attn(),
              /*dropout_p=*/multihead_attn_module->options.dropout(),
              /*out_proj_weight=*/multihead_attn_module->out_proj->weight,
              /*out_proj_bias=*/multihead_attn_module->out_proj->bias
            )
            .training(multihead_attn_module->is_training())
            .key_padding_mask(key_padding_mask_tensor)
            .need_weights(true)
            .attn_mask(attn_mask_tensor)
            .use_separate_proj_weight(true)
            .q_proj_weight(multihead_attn_module->q_proj_weight)
            .k_proj_weight(multihead_attn_module->k_proj_weight)
            .v_proj_weight(multihead_attn_module->v_proj_weight)
            .static_k(saved_k_tensor)
            .static_v(saved_v_tensor)
          );
        }
        result = result.squeeze(0).detach();
        torch::Tensor q_proj_weight;
        torch::Tensor k_proj_weight;
        torch::Tensor v_proj_weight;
        if (multihead_attn_module->_qkv_same_embed_dim) {
          q_proj_weight = multihead_attn_module->in_proj_weight.slice(/*dim=*/0, 0, d_model);
          k_proj_weight = multihead_attn_module->in_proj_weight.slice(/*dim=*/0, d_model, (d_model * 2));
          v_proj_weight = multihead_attn_module->in_proj_weight.slice(/*dim=*/0, (d_model * 2));
        } else {
          q_proj_weight = multihead_attn_module->q_proj_weight;
          k_proj_weight = multihead_attn_module->k_proj_weight;
          v_proj_weight = multihead_attn_module->v_proj_weight;
        }
        auto Q_fc = _fc(Q, q_proj_weight, multihead_attn_module->in_proj_bias.slice(/*dim=*/0, 0, d_model));
        auto K_fc = _fc(K, k_proj_weight, multihead_attn_module->in_proj_bias.slice(/*dim=*/0, d_model, (d_model * 2)));
        auto V_fc = _fc(V, v_proj_weight, multihead_attn_module->in_proj_bias.slice(/*dim=*/0, (d_model * 2)));
  
        if (add_bias_kv) {
          K_fc = torch::cat({K_fc, bias_k.repeat({K_fc.size(0) / bias_k.size(0), 1, 1}/*, axis=0*/)}, /*dim=*/1);
          V_fc = torch::cat({V_fc, bias_v.repeat({V_fc.size(0) / bias_v.size(0), 1, 1}/*, axis=0*/)}, /*dim=*/1);
          if (attn_mask.defined()) {
            attn_mask = torch::cat({attn_mask, torch::ones({1, 1})}, /*dim=*/1);
          }
          if (key_padding_mask.defined()) {
            key_padding_mask = torch::cat({key_padding_mask, torch::full({batch_sz, 1}, false, torch::kBool)}, /*dim=*/1);
          }
          dims[1] += 1;
        }
        const auto Q_split = _split_heads_ref(
          Q_fc, {batch_sz, 1, d_model}, nheads, d_head
        );
        torch::Tensor K_split;
        if (saved_k.defined()) {
          K_split = saved_k.reshape({dims[0], nheads, dims[1], d_head});
        } else {
          K_split = _split_heads_ref(K_fc, dims, nheads, d_head);
        }
        torch::Tensor V_split;
        if (saved_v.defined()) {
          V_split = saved_v.reshape({dims[0], nheads, dims[1], d_head});
        } else {
          V_split = _split_heads_ref(V_fc, dims, nheads, d_head);
        }
        if (add_zero_attn) {
          dims[1] += 1;
          K_split = torch::cat({
            K_split,
            torch::zeros({K_split.size(0), K_split.size(1), 1, K_split.size(3)})
          }, /*dim=*/2);
          V_split = torch::cat({
            V_split,
            torch::zeros({V_split.size(0), V_split.size(1), 1, V_split.size(3)})
          }, /*dim=*/2);
          if (attn_mask.defined()) {
            attn_mask = torch::cat({attn_mask, torch::ones({1, 1})}, /*dim=*/1);
          }
          if (key_padding_mask.defined()) {
            key_padding_mask = torch::cat({key_padding_mask, torch::full({batch_sz, 1}, false, torch::kBool)}, /*dim=*/1);
          }
        }
        torch::Tensor attn_heads;
        torch::Tensor ref_attn_weight;
        std::tie(attn_heads, ref_attn_weight) = _scaled_dot_attn_ref(
            Q_split,
            K_split,
            V_split,
            Q_split.sizes(),
            attn_mask,
            key_padding_mask
        );
        const auto combined_attn_heads = _combine_heads_ref(attn_heads, {batch_sz, 1}, nheads, d_head);
        auto reference = _fc(combined_attn_heads, multihead_attn_module->out_proj->weight, multihead_attn_module->out_proj->bias);
        // NOLINTNEXTLINE(bugprone-argument-comment)
        reference = torch::squeeze(reference, /*axis=*/1);
  
        // result = reference
        ASSERT_EQ(result.sizes(), std::vector<int64_t>({batch_sz, d_model}));
        ASSERT_TRUE(torch::allclose(result, reference, 1e-5, 1e-5, /*equal_nan=*/true));
  
        // result_weight = ref_attn_weight
        result_weight = result_weight.detach();
        ASSERT_EQ(result_weight.sizes(), ref_attn_weight.sizes());
        ASSERT_TRUE(torch::allclose(result_weight, ref_attn_weight, 1e-5, 1e-5, /*equal_nan=*/true));
      }
    }
  }
  
  TEST_F(ModulesTest, MultiheadAttention) {
    using namespace ::detail;
  
    // test_multihead_attn_add_zero_attn
    _multihead_attn_test_helper(
      /*add_key_padding_mask=*/false,
      /*add_bias_kv=*/false,
      /*add_zero_attn=*/true,
      /*saved_kv=*/false,
      /*same_embed_dim=*/false
    );
  
    // test_multihead_attn_add_bias_kv
    _multihead_attn_test_helper(
      /*add_key_padding_mask=*/false,
      /*add_bias_kv=*/true,
      /*add_zero_attn=*/false,
      /*saved_kv=*/false,
      /*same_embed_dim=*/false
    );
  
    // test_multihead_attn_no_masking():
    _multihead_attn_test_helper();
  
    // test_multihead_attn_key_padding_mask
    _multihead_attn_test_helper(
      /*add_key_padding_mask=*/true,
      /*add_bias_kv=*/false,
      /*add_zero_attn=*/false,
      /*saved_kv=*/false,
      /*same_embed_dim=*/false
    );
  
    // test_multihead_attn_saved_kv
    _multihead_attn_test_helper(
      /*add_key_padding_mask=*/false,
      /*add_bias_kv=*/false,
      /*add_zero_attn=*/false,
      /*saved_kv=*/true,
      /*same_embed_dim=*/false
    );
  
    // test_multihead_attn_add_bias_kv_zero_attn
    _multihead_attn_test_helper(
      /*add_key_padding_mask=*/true,
      /*add_bias_kv=*/true,
      /*add_zero_attn=*/true,
      /*saved_kv=*/false,
      /*same_embed_dim=*/false
    );
  
    // test_multihead_attn_all_arguments1
    _multihead_attn_test_helper(
      /*add_key_padding_mask=*/true,
      /*add_bias_kv=*/false,
      /*add_zero_attn=*/true,
      /*saved_kv=*/true,
      /*same_embed_dim=*/false
    );
  
    ASSERT_THROWS_WITH(
      // test_multihead_attn_all_arguments2
      _multihead_attn_test_helper(
        /*add_key_padding_mask=*/true,
        /*add_bias_kv=*/true,
        /*add_zero_attn=*/true,
        /*saved_kv=*/true,
        /*same_embed_dim=*/false
      ),
      "bias cannot be added to static key"
    );
  
    // test_multihead_attn_all_arguments3
    _multihead_attn_test_helper(
      /*add_key_padding_mask=*/true,
      /*add_bias_kv=*/false,
      /*add_zero_attn=*/true,
      /*saved_kv=*/true,
      /*same_embed_dim=*/true
    );
  }
  
  TEST_F(ModulesTest, PrettyPrintIdentity) {
    ASSERT_EQ(c10::str(Identity()), "torch::nn::Identity()");
  }
  
  TEST_F(ModulesTest, PrettyPrintFlatten) {
    ASSERT_EQ(c10::str(Flatten()),
      "torch::nn::Flatten(start_dim=1, end_dim=-1)");
    ASSERT_EQ(c10::str(Flatten(FlattenOptions().start_dim(2).end_dim(4))),
      "torch::nn::Flatten(start_dim=2, end_dim=4)");
  }
  
  TEST_F(ModulesTest, PrettyPrintUnflatten) {
    ASSERT_EQ(
        c10::str(Unflatten(UnflattenOptions(0, {2, 2}))),
        "torch::nn::Unflatten(dim=0, unflattened_size={2, 2})");
    ASSERT_EQ(
        c10::str(Unflatten(UnflattenOptions(
            "B",
            {std::pair<std::string, int64_t>{"B1", 2},
             std::pair<std::string, int64_t>{"B2", 2}}))),
        "torch::nn::Unflatten(dim=\"B\", unflattened_size={{\"B1\", 2}, {\"B2\", 2}})");
  }
  
  TEST_F(ModulesTest, ReflectionPad1d) {
    {
      ReflectionPad1d m(ReflectionPad1dOptions(2));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
      auto output = m(input);
      auto expected = torch::tensor({{{2., 1., 0., 1., 2., 3., 2., 1.},
                                      {6., 5., 4., 5., 6., 7., 6., 5.}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ReflectionPad1d m(ReflectionPad1dOptions({3, 1}));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
      auto output = m(input);
      auto expected = torch::tensor({{{3., 2., 1., 0., 1., 2., 3., 2.},
                                      {7., 6., 5., 4., 5., 6., 7., 6.}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, ReflectionPad2d) {
    {
      ReflectionPad2d m(ReflectionPad2dOptions(2));
      auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
      auto output = m(input);
      auto expected = torch::tensor({{{{8., 7., 6., 7., 8., 7., 6.},
                                       {5., 4., 3., 4., 5., 4., 3.},
                                       {2., 1., 0., 1., 2., 1., 0.},
                                       {5., 4., 3., 4., 5., 4., 3.},
                                       {8., 7., 6., 7., 8., 7., 6.},
                                       {5., 4., 3., 4., 5., 4., 3.},
                                       {2., 1., 0., 1., 2., 1., 0.}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ReflectionPad2d m(ReflectionPad2dOptions({1, 1, 2, 0}));
      auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
      auto output = m(input);
      auto expected = torch::tensor({{{{7., 6., 7., 8., 7.},
                                       {4., 3., 4., 5., 4.},
                                       {1., 0., 1., 2., 1.},
                                       {4., 3., 4., 5., 4.},
                                       {7., 6., 7., 8., 7.}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, ReflectionPad3d) {
    {
      ReflectionPad3d m(ReflectionPad3dOptions(1));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
      auto output = m(input);
      auto expected = torch::tensor({{{{{7., 6., 7., 6.},
                                        {5., 4., 5., 4.},
                                        {7., 6., 7., 6.},
                                        {5., 4., 5., 4.}},
                                       {{3., 2., 3., 2.},
                                        {1., 0., 1., 0.},
                                        {3., 2., 3., 2.},
                                        {1., 0., 1., 0.}},
                                       {{7., 6., 7., 6.},
                                        {5., 4., 5., 4.},
                                        {7., 6., 7., 6.},
                                        {5., 4., 5., 4.}},
                                       {{3., 2., 3., 2.},
                                        {1., 0., 1., 0.},
                                        {3., 2., 3., 2.},
                                        {1., 0., 1., 0.}}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ReflectionPad3d m(ReflectionPad3dOptions({0, 1, 1, 0, 1, 2}));
      auto input = torch::arange(16, torch::kFloat).reshape({1, 1, 4, 2, 2});
      auto output = m(input);
      auto expected = torch::tensor({{{{{6., 7., 6.},
                                        {4., 5., 4.},
                                        {6., 7., 6.}},
                                       {{2., 3., 2.},
                                        {0., 1., 0.},
                                        {2., 3., 2.}},
                                       {{6., 7., 6.},
                                        {4., 5., 4.},
                                        {6., 7., 6.}},
                                       {{10., 11., 10.},
                                        {8., 9., 8.},
                                        {10., 11., 10.}},
                                       {{14., 15., 14.},
                                        {12., 13., 12.},
                                        {14., 15., 14.}},
                                       {{10., 11., 10.},
                                        {8., 9., 8.},
                                        {10., 11., 10.}},
                                       {{6., 7., 6.},
                                        {4., 5., 4.},
                                        {6., 7., 6.}}}}}, torch::kFloat);
      ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 1, 7, 3, 3}));
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  TEST_F(ModulesTest, ReplicationPad1d) {
    {
      ReplicationPad1d m(ReplicationPad1dOptions(2));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
      auto output = m(input);
      auto expected = torch::tensor({{{0., 0., 0., 1., 2., 3., 3., 3.},
                                      {4., 4., 4., 5., 6., 7., 7., 7.}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ReplicationPad1d m(ReplicationPad1dOptions({3, 1}));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
      auto output = m(input);
      auto expected = torch::tensor({{{0., 0., 0., 0., 1., 2., 3., 3.},
                                      {4., 4., 4., 4., 5., 6., 7., 7.}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, ReplicationPad2d) {
    {
      ReplicationPad2d m(ReplicationPad2dOptions(2));
      auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
      auto output = m(input);
      auto expected = torch::tensor({{{{0., 0., 0., 1., 2., 2., 2.},
                                       {0., 0., 0., 1., 2., 2., 2.},
                                       {0., 0., 0., 1., 2., 2., 2.},
                                       {3., 3., 3., 4., 5., 5., 5.},
                                       {6., 6., 6., 7., 8., 8., 8.},
                                       {6., 6., 6., 7., 8., 8., 8.},
                                       {6., 6., 6., 7., 8., 8., 8.}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ReplicationPad2d m(ReplicationPad2dOptions({1, 1, 2, 0}));
      auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
      auto output = m(input);
      auto expected = torch::tensor({{{{0., 0., 1., 2., 2.},
                                       {0., 0., 1., 2., 2.},
                                       {0., 0., 1., 2., 2.},
                                       {3., 3., 4., 5., 5.},
                                       {6., 6., 7., 8., 8.}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, ReplicationPad3d) {
    {
      ReplicationPad3d m(ReplicationPad3dOptions(1));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
      auto output = m(input);
      auto expected = torch::tensor({{{{{0., 0., 1., 1.},
                                        {0., 0., 1., 1.},
                                        {2., 2., 3., 3.},
                                        {2., 2., 3., 3.}},
                                       {{0., 0., 1., 1.},
                                        {0., 0., 1., 1.},
                                        {2., 2., 3., 3.},
                                        {2., 2., 3., 3.}},
                                       {{4., 4., 5., 5.},
                                        {4., 4., 5., 5.},
                                        {6., 6., 7., 7.},
                                        {6., 6., 7., 7.}},
                                       {{4., 4., 5., 5.},
                                        {4., 4., 5., 5.},
                                        {6., 6., 7., 7.},
                                        {6., 6., 7., 7.}}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ReplicationPad3d m(ReplicationPad3dOptions({1, 2, 1, 2, 1, 2}));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
      auto output = m(input);
      auto expected = torch::tensor({{{{{0., 0., 1., 1., 1.},
                                        {0., 0., 1., 1., 1.},
                                        {2., 2., 3., 3., 3.},
                                        {2., 2., 3., 3., 3.},
                                        {2., 2., 3., 3., 3.}},
                                       {{0., 0., 1., 1., 1.},
                                        {0., 0., 1., 1., 1.},
                                        {2., 2., 3., 3., 3.},
                                        {2., 2., 3., 3., 3.},
                                        {2., 2., 3., 3., 3.}},
                                       {{4., 4., 5., 5., 5.},
                                        {4., 4., 5., 5., 5.},
                                        {6., 6., 7., 7., 7.},
                                        {6., 6., 7., 7., 7.},
                                        {6., 6., 7., 7., 7.}},
                                       {{4., 4., 5., 5., 5.},
                                        {4., 4., 5., 5., 5.},
                                        {6., 6., 7., 7., 7.},
                                        {6., 6., 7., 7., 7.},
                                        {6., 6., 7., 7., 7.}},
                                       {{4., 4., 5., 5., 5.},
                                        {4., 4., 5., 5., 5.},
                                        {6., 6., 7., 7., 7.},
                                        {6., 6., 7., 7., 7.},
                                        {6., 6., 7., 7., 7.}}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, ZeroPad2d) {
    {
      ZeroPad2d m(ZeroPad2dOptions(2));
      auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
      auto output = m(input);
      auto expected = torch::tensor({{{{0., 0., 0., 0., 0., 0., 0.},
                                       {0., 0., 0., 0., 0., 0., 0.},
                                       {0., 0., 0., 1., 2., 0., 0.},
                                       {0., 0., 3., 4., 5., 0., 0.},
                                       {0., 0., 6., 7., 8., 0., 0.},
                                       {0., 0., 0., 0., 0., 0., 0.},
                                       {0., 0., 0., 0., 0., 0., 0.}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ZeroPad2d m(ZeroPad2dOptions({1, 1, 2, 0}));
      auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
      auto output = m(input);
      auto expected = torch::tensor({{{{0., 0., 0., 0., 0.},
                                       {0., 0., 0., 0., 0.},
                                       {0., 0., 1., 2., 0.},
                                       {0., 3., 4., 5., 0.},
                                       {0., 6., 7., 8., 0.}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, ConstantPad1d) {
    {
      ConstantPad1d m(ConstantPad1dOptions(2, 3.5));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
      auto output = m(input);
      auto expected = torch::tensor({{{3.5000, 3.5000, 0.0000, 1.0000, 2.0000, 3.0000, 3.5000, 3.5000},
                                      {3.5000, 3.5000, 4.0000, 5.0000, 6.0000, 7.0000, 3.5000, 3.5000}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ConstantPad1d m(ConstantPad1dOptions({3, 1}, 3.5));
      auto input = torch::arange(6, torch::kFloat).reshape({1, 2, 3});
      auto output = m(input);
      auto expected = torch::tensor({{{3.5000, 3.5000, 3.5000, 0.0000, 1.0000, 2.0000, 3.5000},
                                      {3.5000, 3.5000, 3.5000, 3.0000, 4.0000, 5.0000, 3.5000}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, ConstantPad2d) {
    {
      ConstantPad2d m(ConstantPad2dOptions(2, 3.5));
      auto input = torch::arange(4, torch::kFloat).reshape({1, 2, 2});
      auto output = m(input);
      auto expected = torch::tensor({{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                      {3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                      {3.5000, 3.5000, 0.0000, 1.0000, 3.5000, 3.5000},
                                      {3.5000, 3.5000, 2.0000, 3.0000, 3.5000, 3.5000},
                                      {3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                      {3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ConstantPad2d m(ConstantPad2dOptions({3, 0, 2, 1}, 3.5));
      auto input = torch::arange(4, torch::kFloat).reshape({1, 2, 2});
      auto output = m(input);
      auto expected = torch::tensor({{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                      {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                      {3.5000, 3.5000, 3.5000, 0.0000, 1.0000},
                                      {3.5000, 3.5000, 3.5000, 2.0000, 3.0000},
                                      {3.5000, 3.5000, 3.5000, 3.5000, 3.5000}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, ConstantPad3d) {
    {
      ConstantPad3d m(ConstantPad3dOptions(1, 3.5));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
      auto output = m(input);
      auto expected = torch::tensor({{{{{3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000}},
                                       {{3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 0.0000, 1.0000, 3.5000},
                                        {3.5000, 2.0000, 3.0000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000}},
                                       {{3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 4.0000, 5.0000, 3.5000},
                                        {3.5000, 6.0000, 7.0000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000}},
                                       {{3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000}}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
    {
      ConstantPad3d m(ConstantPad3dOptions({1, 2, 1, 2, 1, 2}, 3.5));
      auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
      auto output = m(input);
      auto expected = torch::tensor({{{{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000}},
                                       {{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 0.0000, 1.0000, 3.5000, 3.5000},
                                        {3.5000, 2.0000, 3.0000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000}},
                                       {{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 4.0000, 5.0000, 3.5000, 3.5000},
                                        {3.5000, 6.0000, 7.0000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000}},
                                       {{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000}},
                                       {{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
                                        {3.5000, 3.5000, 3.5000, 3.5000, 3.5000}}}}}, torch::kFloat);
      ASSERT_TRUE(output.allclose(expected));
    }
  }
  
  TEST_F(ModulesTest, CrossMapLRN2d) {
    /// size 3, default options
    auto input = torch::arange(9, torch::kFloat32).view({1, 1, 3, 3}).requires_grad_(true);
    auto expected = torch::tensor({{{{0.00000000, 0.99997497, 1.99980010},
                                     {2.99932500, 3.99840070, 4.99687700},
                                     {5.99460600, 6.99143740, 7.98722360}}}}, torch::kFloat32);
    auto grad_expected = torch::tensor({{{{1.00000000, 0.99992496, 0.99970007},
                                          {0.99932520, 0.99880093, 0.99812720},
                                          {0.99730474, 0.99633380, 0.99521490}}}}, torch::kFloat32);
    auto crossmaplrn2d = CrossMapLRN2d(3);
    auto output = crossmaplrn2d(input);
    output.sum().backward();
  
    ASSERT_TRUE(input.grad().allclose(grad_expected));
    ASSERT_TRUE(output.allclose(expected));
  
    /// size change
    crossmaplrn2d = CrossMapLRN2d(CrossMapLRN2dOptions(4).alpha(1e-4).beta(0.75).k(1));
    output = crossmaplrn2d(input);
    expected = torch::tensor({{{{0.00000000, 0.99998120, 1.99985000},
                                {2.99949400, 3.99880050, 4.99765800},
                                {5.99595300, 6.99357600, 7.99041300}}}}, torch::kFloat32);
    ASSERT_TRUE(output.allclose(expected));
  
    /// alpha change
    crossmaplrn2d = CrossMapLRN2d(CrossMapLRN2dOptions(3).alpha(1e-3).beta(0.75).k(1));
    output = crossmaplrn2d(input);
    expected = torch::tensor({{{{0.00000000, 0.99975010, 1.99800230},
                                {2.99326750, 3.98407440, 4.96897600},
                                {5.94656100, 6.91545720, 7.87434340}}}}, torch::kFloat32);
    ASSERT_TRUE(output.allclose(expected));
  
    /// beta change
    crossmaplrn2d = CrossMapLRN2d(CrossMapLRN2dOptions(3).alpha(1e-4).beta(0.95).k(1));
    output = crossmaplrn2d(input);
    expected = torch::tensor({{{{0.00000000, 0.99996830, 1.99974680},
                                {2.99914500, 3.99797440, 4.99604460},
                                {5.99316840, 6.98915600, 7.98382000}}}}, torch::kFloat32);
    ASSERT_TRUE(output.allclose(expected));
  
    /// k change
    crossmaplrn2d = CrossMapLRN2d(CrossMapLRN2dOptions(3).alpha(1e-4).beta(0.75).k(2));
    output = crossmaplrn2d(input);
    expected = torch::tensor({{{{0.00000000, 0.59459610, 1.18914770},
                                {1.78361000, 2.37793870, 2.97208900},
                                {3.56601700, 4.15967700, 4.75302650}}}}, torch::kFloat32);
    ASSERT_TRUE(output.allclose(expected));
  }
  
  TEST_F(ModulesTest, RNNCell) {
    torch::manual_seed(0);
    auto rnn = RNNCell(1, 2);
    auto input = torch::randn({3, 1});
    auto hx = torch::randn({3, 2});
    auto output = rnn(input, hx);
    auto expected = torch::tensor({{-0.5078,  0.4380},
                                   {-0.7215,  0.2969},
                                   {-0.1304,  0.0653}});
    ASSERT_TRUE(torch::allclose(output, expected, 1e-05, 2e-04));
  
    output = rnn(input);
    expected = torch::tensor({{-0.0775,  0.6688},
                              {-0.0734,  0.4759},
                              {-0.0725,  0.4225}});
    ASSERT_TRUE(torch::allclose(output, expected, 1e-05, 2e-04));
  }
  
  TEST_F(ModulesTest, LSTMCell) {
    torch::manual_seed(0);
    auto rnn = LSTMCell(1, 2);
    auto input = torch::randn({3, 1});
    auto hx = torch::randn({3, 2});
    auto cx = torch::randn({3, 2});
    auto output = rnn(input, std::make_tuple(hx, cx));
    auto output_hx = std::get<0>(output);
    auto output_cx = std::get<1>(output);
    auto expected_hx = torch::tensor({{-0.2462,  0.0810},
                                      {-0.2206,  0.1867},
                                      {-0.0146,  0.0429}});
    auto expected_cx = torch::tensor({{-0.4480,  0.1071},
                                      {-0.6245,  0.2687},
                                      {-0.0322,  0.0518}});
    ASSERT_TRUE(torch::allclose(output_hx, expected_hx, 1e-05, 2e-04));
    ASSERT_TRUE(torch::allclose(output_cx, expected_cx, 1e-05, 2e-04));
  
    output = rnn(input);
    output_hx = std::get<0>(output);
    output_cx = std::get<1>(output);
    expected_hx = torch::tensor({{-0.1331,  0.1634},
                                 {-0.1494,  0.2869},
                                 {-0.1428,  0.2263}});
    expected_cx = torch::tensor({{-0.2679,  0.2180},
                                 {-0.3049,  0.3493},
                                 {-0.2896,  0.2853}});
    ASSERT_TRUE(torch::allclose(output_hx, expected_hx, 1e-05, 2e-04));
    ASSERT_TRUE(torch::allclose(output_cx, expected_cx, 1e-05, 2e-04));
  }
  
  TEST_F(ModulesTest, GRUCell) {
    torch::manual_seed(0);
    auto rnn = GRUCell(1, 2);
    auto input = torch::randn({3, 1});
    auto hx = torch::randn({3, 2});
    auto output = rnn(input, hx);
    auto expected = torch::tensor({{ 1.0243,  0.3227},
                                   {-0.5659,  0.0330},
                                   {-0.4030, -0.2800}});
    ASSERT_TRUE(torch::allclose(output, expected, 1e-05, 2e-04));
  
    output = rnn(input);
    expected = torch::tensor({{-0.0085,  0.1095},
                              {-0.1291,  0.2675},
                              {-0.1339,  0.2725}});
    ASSERT_TRUE(torch::allclose(output, expected, 1e-05, 2e-04));
  }
  
  TEST_F(ModulesTest, PrettyPrintLinear) {
    ASSERT_EQ(
        c10::str(Linear(3, 4)), "torch::nn::Linear(in_features=3, out_features=4, bias=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintBilinear) {
    ASSERT_EQ(
        c10::str(Bilinear(3, 2, 4)), "torch::nn::Bilinear(in1_features=3, in2_features=2, out_features=4, bias=true)");
    ASSERT_EQ(
        c10::str(Bilinear(BilinearOptions(3, 2, 4).bias(false))), "torch::nn::Bilinear(in1_features=3, in2_features=2, out_features=4, bias=false)");
  }
  
  TEST_F(ModulesTest, PrettyPrintConv) {
    ASSERT_EQ(
        c10::str(Conv1d(3, 4, 5)),
        "torch::nn::Conv1d(3, 4, kernel_size=5, stride=1)");
  
    ASSERT_EQ(
        c10::str(Conv2d(3, 4, 5)),
        "torch::nn::Conv2d(3, 4, kernel_size=[5, 5], stride=[1, 1])");
    ASSERT_EQ(
        c10::str(Conv2d(Conv2dOptions(3, 4, 5).stride(2))),
        "torch::nn::Conv2d(3, 4, kernel_size=[5, 5], stride=[2, 2])");
    {
      const auto options =
          Conv2dOptions(3, 4, std::vector<int64_t>{5, 6}).stride({1, 2});
      ASSERT_EQ(
          c10::str(Conv2d(options)),
          "torch::nn::Conv2d(3, 4, kernel_size=[5, 6], stride=[1, 2])");
    }
  
    ASSERT_EQ(
        c10::str(Conv3d(4, 4, std::vector<int64_t>{5, 6, 7})),
        "torch::nn::Conv3d(4, 4, kernel_size=[5, 6, 7], stride=[1, 1, 1])");
    {
      const auto options =
          Conv3dOptions(4, 4, std::vector<int64_t>{5, 6, 7})
            .stride({1, 2, 3})
            .padding(1)
            .dilation(0)
            .groups(2)
            .bias(false)
            .padding_mode(torch::kCircular);
      ASSERT_EQ(
          c10::str(
            Conv3d(options)),
            "torch::nn::Conv3d("
            "4, "
            "4, "
            "kernel_size=[5, 6, 7], "
            "stride=[1, 2, 3], "
            "padding=[1, 1, 1], "
            "dilation=[0, 0, 0], "
            "groups=2, "
            "bias=false, "
            "padding_mode=kCircular)");
    }
  }
  
  TEST_F(ModulesTest, PrettyPrintConvTranspose) {
    ASSERT_EQ(
        c10::str(ConvTranspose1d(3, 4, 5)),
        "torch::nn::ConvTranspose1d(3, 4, kernel_size=5, stride=1)");
  
    ASSERT_EQ(
        c10::str(ConvTranspose2d(3, 4, 5)),
        "torch::nn::ConvTranspose2d(3, 4, kernel_size=[5, 5], stride=[1, 1])");
    ASSERT_EQ(
        c10::str(ConvTranspose2d(ConvTranspose2dOptions(3, 4, 5).stride(2))),
        "torch::nn::ConvTranspose2d(3, 4, kernel_size=[5, 5], stride=[2, 2])");
    {
      const auto options =
          ConvTranspose2dOptions(3, 4, std::vector<int64_t>{5, 6}).stride({1, 2});
      ASSERT_EQ(
          c10::str(ConvTranspose2d(options)),
          "torch::nn::ConvTranspose2d(3, 4, kernel_size=[5, 6], stride=[1, 2])");
    }
  
    ASSERT_EQ(
        c10::str(ConvTranspose3d(4, 4, std::vector<int64_t>{5, 6, 7})),
        "torch::nn::ConvTranspose3d(4, 4, kernel_size=[5, 6, 7], stride=[1, 1, 1])");
    {
      const auto options =
          ConvTranspose3dOptions(4, 4, std::vector<int64_t>{5, 6, 7})
            .stride({1, 2, 3})
            .padding(1)
            .dilation(0)
            .groups(2)
            .bias(false)
            .padding_mode(torch::kCircular);
      ASSERT_EQ(
          c10::str(
            ConvTranspose3d(options)),
            "torch::nn::ConvTranspose3d("
            "4, "
            "4, "
            "kernel_size=[5, 6, 7], "
            "stride=[1, 2, 3], "
            "padding=[1, 1, 1], "
            "dilation=[0, 0, 0], "
            "groups=2, "
            "bias=false, "
            "padding_mode=kCircular)");
    }
  }
  
  TEST_F(ModulesTest, PrettyPrintUpsample) {
    ASSERT_EQ(
        c10::str(Upsample(UpsampleOptions().size(std::vector<int64_t>({2, 4, 4})))),
        "torch::nn::Upsample(size=[2, 4, 4], mode=kNearest)");
    ASSERT_EQ(
        c10::str(Upsample(UpsampleOptions().scale_factor(std::vector<double>({0.5, 1.5})).mode(torch::kBilinear))),
        "torch::nn::Upsample(scale_factor=[0.5, 1.5], mode=kBilinear)");
  }
  
  TEST_F(ModulesTest, PrettyPrintFold) {
    ASSERT_EQ(
        c10::str(Fold(FoldOptions({2, 2}, {5, 5}))),
        "torch::nn::Fold(output_size=[2, 2], kernel_size=[5, 5], dilation=[1, 1], padding=[0, 0], stride=[1, 1])");
    ASSERT_EQ(
        c10::str(Fold(FoldOptions({8, 8}, {3, 3}).dilation(2).padding({2, 1}).stride(2))),
        "torch::nn::Fold(output_size=[8, 8], kernel_size=[3, 3], dilation=[2, 2], padding=[2, 1], stride=[2, 2])");
  }
  
  TEST_F(ModulesTest, PrettyPrintUnfold) {
    ASSERT_EQ(
        c10::str(Unfold(torch::IntArrayRef({2, 4}))),
        "torch::nn::Unfold(kernel_size=[2, 4], dilation=[1, 1], padding=[0, 0], stride=[1, 1])");
    ASSERT_EQ(
        c10::str(Unfold(UnfoldOptions({2, 4}).dilation(2).padding({2, 1}).stride(2))),
        "torch::nn::Unfold(kernel_size=[2, 4], dilation=[2, 2], padding=[2, 1], stride=[2, 2])");
  }
  
  TEST_F(ModulesTest, PrettyPrintMaxPool) {
    ASSERT_EQ(
        c10::str(MaxPool1d(5)),
        "torch::nn::MaxPool1d(kernel_size=5, stride=5, padding=0, dilation=1, ceil_mode=false)");
    ASSERT_EQ(
        c10::str(MaxPool2d(5)),
        "torch::nn::MaxPool2d(kernel_size=[5, 5], stride=[5, 5], padding=[0, 0], dilation=[1, 1], ceil_mode=false)");
    ASSERT_EQ(
        c10::str(MaxPool2d(MaxPool2dOptions(5).stride(2))),
        "torch::nn::MaxPool2d(kernel_size=[5, 5], stride=[2, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=false)");
    ASSERT_EQ(
        c10::str(MaxPool3d(5)),
        "torch::nn::MaxPool3d(kernel_size=[5, 5, 5], stride=[5, 5, 5], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=false)");
    ASSERT_EQ(
        c10::str(MaxPool3d(MaxPool3dOptions(5).stride(2))),
        "torch::nn::MaxPool3d(kernel_size=[5, 5, 5], stride=[2, 2, 2], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=false)");
  
    const auto options =
        MaxPool2dOptions(std::vector<int64_t>{5, 6}).stride({1, 2});
    ASSERT_EQ(
        c10::str(MaxPool2d(options)),
        "torch::nn::MaxPool2d(kernel_size=[5, 6], stride=[1, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=false)");
  }
  
  TEST_F(ModulesTest, PrettyPrintAvgPool) {
    ASSERT_EQ(
        c10::str(AvgPool1d(5)),
        "torch::nn::AvgPool1d(kernel_size=5, stride=5, padding=0)");
    ASSERT_EQ(
        c10::str(AvgPool2d(5)),
        "torch::nn::AvgPool2d(kernel_size=[5, 5], stride=[5, 5], padding=[0, 0])");
    ASSERT_EQ(
        c10::str(AvgPool2d(AvgPool2dOptions(5).stride(2))),
        "torch::nn::AvgPool2d(kernel_size=[5, 5], stride=[2, 2], padding=[0, 0])");
    ASSERT_EQ(
        c10::str(AvgPool3d(5)),
        "torch::nn::AvgPool3d(kernel_size=[5, 5, 5], stride=[5, 5, 5], padding=[0, 0, 0])");
    ASSERT_EQ(
        c10::str(AvgPool3d(AvgPool3dOptions(5).stride(2))),
        "torch::nn::AvgPool3d(kernel_size=[5, 5, 5], stride=[2, 2, 2], padding=[0, 0, 0])");
  
    const auto options =
        AvgPool2dOptions(std::vector<int64_t>{5, 6}).stride({1, 2});
    ASSERT_EQ(
        c10::str(AvgPool2d(options)),
        "torch::nn::AvgPool2d(kernel_size=[5, 6], stride=[1, 2], padding=[0, 0])");
  }
  
  TEST_F(ModulesTest, PrettyPrinFractionalMaxPool) {
    ASSERT_EQ(
        c10::str(FractionalMaxPool2d(FractionalMaxPool2dOptions(5).output_size(1))),
        "torch::nn::FractionalMaxPool2d()");
    ASSERT_EQ(
        c10::str(FractionalMaxPool3d(FractionalMaxPool3dOptions(5).output_size(1))),
        "torch::nn::FractionalMaxPool3d()");
  }
  
  TEST_F(ModulesTest, PrettyPrintLPPool) {
    ASSERT_EQ(
        c10::str(LPPool1d(2, 5)),
        "torch::nn::LPPool1d(norm_type=2, kernel_size=5, stride=5, ceil_mode=false)");
    ASSERT_EQ(
        c10::str(LPPool1d(LPPool1dOptions(1, 2).stride(5).ceil_mode(true))),
        "torch::nn::LPPool1d(norm_type=1, kernel_size=2, stride=5, ceil_mode=true)");
    ASSERT_EQ(
        c10::str(LPPool2d(2, std::vector<int64_t>({1, 2}))),
        "torch::nn::LPPool2d(norm_type=2, kernel_size=[1, 2], stride=[1, 2], ceil_mode=false)");
    ASSERT_EQ(
        c10::str(LPPool2d(LPPool2dOptions(1, std::vector<int64_t>({3, 4})).stride({5, 6}).ceil_mode(true))),
        "torch::nn::LPPool2d(norm_type=1, kernel_size=[3, 4], stride=[5, 6], ceil_mode=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintAdaptiveMaxPool) {
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool1d(5)),
        "torch::nn::AdaptiveMaxPool1d(output_size=5)");
  
    const auto options = AdaptiveMaxPool1dOptions(3);
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool1d(options)),
        "torch::nn::AdaptiveMaxPool1d(output_size=3)");
  
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool2d(5)),
        "torch::nn::AdaptiveMaxPool2d(output_size=[5, 5])");
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({5, 6}))),
        "torch::nn::AdaptiveMaxPool2d(output_size=[5, 6])");
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({5, c10::nullopt}))),
        "torch::nn::AdaptiveMaxPool2d(output_size=[5, None])");
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({c10::nullopt, c10::nullopt}))),
        "torch::nn::AdaptiveMaxPool2d(output_size=[None, None])");
  
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool3d(5)),
        "torch::nn::AdaptiveMaxPool3d(output_size=[5, 5, 5])");
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({5, 6, 7}))),
        "torch::nn::AdaptiveMaxPool3d(output_size=[5, 6, 7])");
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({5, c10::nullopt, 7}))),
        "torch::nn::AdaptiveMaxPool3d(output_size=[5, None, 7])");
    ASSERT_EQ(
        c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({c10::nullopt, c10::nullopt, c10::nullopt}))),
        "torch::nn::AdaptiveMaxPool3d(output_size=[None, None, None])");
  }
  
  TEST_F(ModulesTest, PrettyPrintAdaptiveAvgPool) {
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool1d(5)),
        "torch::nn::AdaptiveAvgPool1d(output_size=5)");
  
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool2d(5)),
        "torch::nn::AdaptiveAvgPool2d(output_size=[5, 5])");
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({5, 6}))),
        "torch::nn::AdaptiveAvgPool2d(output_size=[5, 6])");
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({5, c10::nullopt}))),
        "torch::nn::AdaptiveAvgPool2d(output_size=[5, None])");
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({c10::nullopt, c10::nullopt}))),
        "torch::nn::AdaptiveAvgPool2d(output_size=[None, None])");
  
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool3d(5)),
        "torch::nn::AdaptiveAvgPool3d(output_size=[5, 5, 5])");
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({5, 6, 7}))),
        "torch::nn::AdaptiveAvgPool3d(output_size=[5, 6, 7])");
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({5, c10::nullopt, 7}))),
        "torch::nn::AdaptiveAvgPool3d(output_size=[5, None, 7])");
    ASSERT_EQ(
        c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({c10::nullopt, c10::nullopt, c10::nullopt}))),
        "torch::nn::AdaptiveAvgPool3d(output_size=[None, None, None])");
  }
  
  TEST_F(ModulesTest, PrettyPrintMaxUnpool) {
    ASSERT_EQ(
        c10::str(MaxUnpool1d(5)),
        "torch::nn::MaxUnpool1d(kernel_size=5, stride=5, padding=0)");
    ASSERT_EQ(
        c10::str(MaxUnpool1d(MaxUnpool1dOptions(5).stride(3).padding(1))),
        "torch::nn::MaxUnpool1d(kernel_size=5, stride=3, padding=1)");
  
    ASSERT_EQ(
        c10::str(MaxUnpool2d(5)),
        "torch::nn::MaxUnpool2d(kernel_size=[5, 5], stride=[5, 5], padding=[0, 0])");
    ASSERT_EQ(
        c10::str(MaxUnpool2d(std::vector<int64_t>{5, 6})),
        "torch::nn::MaxUnpool2d(kernel_size=[5, 6], stride=[5, 6], padding=[0, 0])");
    ASSERT_EQ(
        c10::str(MaxUnpool2d(MaxUnpool2dOptions(std::vector<int64_t>{5, 6}).stride({3, 4}).padding({1, 2}))),
        "torch::nn::MaxUnpool2d(kernel_size=[5, 6], stride=[3, 4], padding=[1, 2])");
  }
  
  TEST_F(ModulesTest, PrettyPrintDropout) {
    ASSERT_EQ(c10::str(Dropout()), "torch::nn::Dropout(p=0.5, inplace=false)");
    ASSERT_EQ(c10::str(Dropout(0.42)), "torch::nn::Dropout(p=0.42, inplace=false)");
    ASSERT_EQ(c10::str(Dropout(DropoutOptions().p(0.42).inplace(true))), "torch::nn::Dropout(p=0.42, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintDropout2d) {
    ASSERT_EQ(c10::str(Dropout2d()), "torch::nn::Dropout2d(p=0.5, inplace=false)");
    ASSERT_EQ(c10::str(Dropout2d(0.42)), "torch::nn::Dropout2d(p=0.42, inplace=false)");
    ASSERT_EQ(c10::str(Dropout2d(Dropout2dOptions().p(0.42).inplace(true))), "torch::nn::Dropout2d(p=0.42, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintDropout3d) {
    ASSERT_EQ(c10::str(Dropout3d()), "torch::nn::Dropout3d(p=0.5, inplace=false)");
    ASSERT_EQ(c10::str(Dropout3d(0.42)), "torch::nn::Dropout3d(p=0.42, inplace=false)");
    ASSERT_EQ(c10::str(Dropout3d(Dropout3dOptions().p(0.42).inplace(true))), "torch::nn::Dropout3d(p=0.42, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintFunctional) {
    ASSERT_EQ(c10::str(Functional(torch::relu)), "torch::nn::Functional()");
  }
  
  TEST_F(ModulesTest, PrettyPrintBatchNorm1d) {
    ASSERT_EQ(
        c10::str(BatchNorm1d(
            BatchNorm1dOptions(4).eps(0.5).momentum(0.1).affine(false)
            .track_running_stats(true))),
        "torch::nn::BatchNorm1d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintBatchNorm2d) {
    ASSERT_EQ(
        c10::str(BatchNorm2d(
            BatchNorm2dOptions(4).eps(0.5).momentum(0.1).affine(false)
            .track_running_stats(true))),
        "torch::nn::BatchNorm2d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintBatchNorm3d) {
    ASSERT_EQ(
        c10::str(BatchNorm3d(
            BatchNorm3dOptions(4).eps(0.5).momentum(0.1).affine(false)
            .track_running_stats(true))),
        "torch::nn::BatchNorm3d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintInstanceNorm1d) {
    ASSERT_EQ(
        c10::str(InstanceNorm1d(
            InstanceNorm1dOptions(4).eps(0.5).momentum(0.1).affine(false)
            .track_running_stats(true))),
        "torch::nn::InstanceNorm1d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintInstanceNorm2d) {
    ASSERT_EQ(
        c10::str(InstanceNorm2d(
            InstanceNorm2dOptions(4).eps(0.5).momentum(0.1).affine(false)
            .track_running_stats(true))),
        "torch::nn::InstanceNorm2d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintInstanceNorm3d) {
    ASSERT_EQ(
        c10::str(InstanceNorm3d(
            InstanceNorm3dOptions(4).eps(0.5).momentum(0.1).affine(false)
            .track_running_stats(true))),
        "torch::nn::InstanceNorm3d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintLayerNorm) {
    ASSERT_EQ(
      c10::str(LayerNorm(LayerNormOptions({2, 2}))),
        "torch::nn::LayerNorm([2, 2], eps=1e-05, elementwise_affine=true)");
        ASSERT_EQ(
          c10::str(LayerNorm(LayerNormOptions({2, 2}).elementwise_affine(false).eps(2e-5))),
            "torch::nn::LayerNorm([2, 2], eps=2e-05, elementwise_affine=false)");
  }
  
  TEST_F(ModulesTest, PrettyPrintGroupNorm) {
    ASSERT_EQ(
      c10::str(GroupNorm(GroupNormOptions(2, 2))),
      "torch::nn::GroupNorm(2, 2, eps=1e-05, affine=true)");
    ASSERT_EQ(
      c10::str(GroupNorm(GroupNormOptions(2, 2).eps(2e-5).affine(false))),
      "torch::nn::GroupNorm(2, 2, eps=2e-05, affine=false)");
  }
  
  TEST_F(ModulesTest, PrettyPrintLocalResponseNorm) {
    ASSERT_EQ(
      c10::str(LocalResponseNorm(LocalResponseNormOptions(2))),
        "torch::nn::LocalResponseNorm(2, alpha=0.0001, beta=0.75, k=1)");
    ASSERT_EQ(
      c10::str(LocalResponseNorm(LocalResponseNormOptions(2).alpha(0.0002).beta(0.85).k(2.))),
        "torch::nn::LocalResponseNorm(2, alpha=0.0002, beta=0.85, k=2)");
  }
  
  TEST_F(ModulesTest, PrettyPrintEmbedding) {
    ASSERT_EQ(
        c10::str(Embedding(EmbeddingOptions(10, 2))),
        "torch::nn::Embedding(num_embeddings=10, embedding_dim=2)");
    ASSERT_EQ(
        c10::str(Embedding(EmbeddingOptions(10, 2).padding_idx(3).max_norm(2))),
        "torch::nn::Embedding(num_embeddings=10, embedding_dim=2, padding_idx=3, max_norm=2)");
    ASSERT_EQ(
        c10::str(Embedding(EmbeddingOptions(10, 2).padding_idx(3).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true))),
        "torch::nn::Embedding(num_embeddings=10, embedding_dim=2, padding_idx=3, max_norm=2, norm_type=2.5, scale_grad_by_freq=true, sparse=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintEmbeddingBag) {
    ASSERT_EQ(
        c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2))),
        "torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2)");
    ASSERT_EQ(
        c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2).max_norm(2))),
        "torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2, max_norm=2)");
    ASSERT_EQ(
        c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true))),
        "torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2, max_norm=2, norm_type=2.5, scale_grad_by_freq=true, sparse=true)");
    ASSERT_EQ(
        c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true).mode(torch::kSum))),
        "torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2, max_norm=2, norm_type=2.5, scale_grad_by_freq=true, sparse=true, mode=kSum)");
    ASSERT_EQ(
        c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true).mode(torch::kSum).padding_idx(5))),
        "torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2, max_norm=2, norm_type=2.5, scale_grad_by_freq=true, sparse=true, mode=kSum, padding_idx=5)");
  }
  
  TEST_F(ModulesTest, PrettyPrintL1Loss) {
    ASSERT_EQ(
        c10::str(L1Loss()),
        "torch::nn::L1Loss()");
  }
  TEST_F(ModulesTest, PrettyPrintKLDivLoss) {
    ASSERT_EQ(
        c10::str(KLDivLoss()),
        "torch::nn::KLDivLoss()");
  }
  TEST_F(ModulesTest, PrettyPrintMSELoss) {
    ASSERT_EQ(
        c10::str(MSELoss()),
        "torch::nn::MSELoss()");
  }
  TEST_F(ModulesTest, PrettyPrintBCELoss) {
    ASSERT_EQ(
        c10::str(BCELoss()),
        "torch::nn::BCELoss()");
  }
  TEST_F(ModulesTest, PrettyPrintHingeEmbeddingLoss) {
    ASSERT_EQ(
        c10::str(HingeEmbeddingLoss(HingeEmbeddingLossOptions().margin(4))),
        "torch::nn::HingeEmbeddingLoss(margin=4)");
  }
  
  TEST_F(ModulesTest, PrettyPrintCosineEmbeddingLoss) {
    ASSERT_EQ(
        c10::str(CosineEmbeddingLoss(CosineEmbeddingLossOptions().margin(0.25))),
        "torch::nn::CosineEmbeddingLoss(margin=0.25)");
  }
  
  TEST_F(ModulesTest, PrettyPrintTripletMarginLoss) {
    ASSERT_EQ(
        c10::str(TripletMarginLoss(TripletMarginLossOptions().margin(3).p(2).eps(1e-06).swap(false))),
        "torch::nn::TripletMarginLoss(margin=3, p=2, eps=1e-06, swap=false)");
  }
  
  TEST_F(ModulesTest, PrettyPrintTripletMarginWithDistanceLoss) {
    auto distanceOptions = TripletMarginWithDistanceLossOptions()
                               .distance_function([&](const torch::Tensor& x,
                                                      const torch::Tensor& y) {
                                 return torch::pairwise_distance(x, y, 2.0, 1e-6);
                               })
                               .margin(1.5)
                               .swap(true)
                               .reduction(torch::kMean);
    ASSERT_EQ(
        c10::str(TripletMarginWithDistanceLoss(distanceOptions)),
        "torch::nn::TripletMarginWithDistanceLoss(margin=1.5, swap=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintNLLLoss) {
    ASSERT_EQ(
        c10::str(NLLLoss()), "torch::nn::NLLLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrinCrossEntropyLoss) {
    ASSERT_EQ(
        c10::str(CrossEntropyLoss()), "torch::nn::CrossEntropyLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrintMultiLabelMarginLoss) {
    ASSERT_EQ(c10::str(MultiLabelMarginLoss()), "torch::nn::MultiLabelMarginLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrintMultiLabelSoftMarginLoss) {
    ASSERT_EQ(c10::str(MultiLabelSoftMarginLoss()), "torch::nn::MultiLabelSoftMarginLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrintSoftMarginLoss) {
    ASSERT_EQ(c10::str(SoftMarginLoss()), "torch::nn::SoftMarginLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrintCosineSimilarity) {
    ASSERT_EQ(
        c10::str(CosineSimilarity()),
        "torch::nn::CosineSimilarity(dim=1, eps=1e-08)");
    ASSERT_EQ(
        c10::str(CosineSimilarity(CosineSimilarityOptions().dim(0).eps(0.5))),
        "torch::nn::CosineSimilarity(dim=0, eps=0.5)");
  }
  
  TEST_F(ModulesTest, PrettyPrintPairwiseDistance) {
    ASSERT_EQ(
        c10::str(PairwiseDistance()),
        "torch::nn::PairwiseDistance(p=2, eps=1e-06, keepdim=false)");
    ASSERT_EQ(
        c10::str(PairwiseDistance(PairwiseDistanceOptions().p(3).eps(0.5).keepdim(true))),
        "torch::nn::PairwiseDistance(p=3, eps=0.5, keepdim=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintReflectionPad) {
    ASSERT_EQ(
        c10::str(ReflectionPad1d(ReflectionPad1dOptions(2))),
        "torch::nn::ReflectionPad1d(padding=[2, 2])");
    ASSERT_EQ(
        c10::str(ReflectionPad1d(ReflectionPad1dOptions({3, 1}))),
        "torch::nn::ReflectionPad1d(padding=[3, 1])");
    ASSERT_EQ(
        c10::str(ReflectionPad2d(ReflectionPad2dOptions(2))),
        "torch::nn::ReflectionPad2d(padding=[2, 2, 2, 2])");
    ASSERT_EQ(
        c10::str(ReflectionPad2d(ReflectionPad2dOptions({1, 1, 2, 0}))),
        "torch::nn::ReflectionPad2d(padding=[1, 1, 2, 0])");
  }
  
  TEST_F(ModulesTest, PrettyPrintReplicationPad) {
    ASSERT_EQ(
        c10::str(ReplicationPad1d(ReplicationPad1dOptions(2))),
        "torch::nn::ReplicationPad1d(padding=[2, 2])");
    ASSERT_EQ(
        c10::str(ReplicationPad1d(ReplicationPad1dOptions({3, 1}))),
        "torch::nn::ReplicationPad1d(padding=[3, 1])");
    ASSERT_EQ(
        c10::str(ReplicationPad2d(ReplicationPad2dOptions(2))),
        "torch::nn::ReplicationPad2d(padding=[2, 2, 2, 2])");
    ASSERT_EQ(
        c10::str(ReplicationPad2d(ReplicationPad2dOptions({1, 1, 2, 0}))),
        "torch::nn::ReplicationPad2d(padding=[1, 1, 2, 0])");
    ASSERT_EQ(
        c10::str(ReplicationPad3d(ReplicationPad3dOptions(1))),
        "torch::nn::ReplicationPad3d(padding=[1, 1, 1, 1, 1, 1])");
    ASSERT_EQ(
        c10::str(ReplicationPad3d(ReplicationPad3dOptions({1, 2, 1, 2, 1, 2}))),
        "torch::nn::ReplicationPad3d(padding=[1, 2, 1, 2, 1, 2])");
  }
  
  TEST_F(ModulesTest, PrettyPrintZeroPad2d) {
    ASSERT_EQ(
        c10::str(ZeroPad2d(ZeroPad2dOptions(2))),
        "torch::nn::ZeroPad2d(padding=[2, 2, 2, 2])");
    ASSERT_EQ(
        c10::str(ZeroPad2d(ZeroPad2dOptions({1, 1, 2, 0}))),
        "torch::nn::ZeroPad2d(padding=[1, 1, 2, 0])");
  }
  
  TEST_F(ModulesTest, PrettyPrintConstantPad) {
    ASSERT_EQ(
        c10::str(ConstantPad1d(ConstantPad1dOptions(2, 3.5))),
        "torch::nn::ConstantPad1d(padding=[2, 2], value=3.5)");
    ASSERT_EQ(
        c10::str(ConstantPad1d(ConstantPad1dOptions({3, 1}, 3.5))),
        "torch::nn::ConstantPad1d(padding=[3, 1], value=3.5)");
    ASSERT_EQ(
        c10::str(ConstantPad2d(ConstantPad2dOptions(2, 3.5))),
        "torch::nn::ConstantPad2d(padding=[2, 2, 2, 2], value=3.5)");
    ASSERT_EQ(
        c10::str(ConstantPad2d(ConstantPad2dOptions({3, 0, 2, 1}, 3.5))),
        "torch::nn::ConstantPad2d(padding=[3, 0, 2, 1], value=3.5)");
    ASSERT_EQ(
        c10::str(ConstantPad3d(ConstantPad3dOptions(1, 3.5))),
        "torch::nn::ConstantPad3d(padding=[1, 1, 1, 1, 1, 1], value=3.5)");
    ASSERT_EQ(
        c10::str(ConstantPad3d(ConstantPad3dOptions({1, 2, 1, 2, 1, 2}, 3.5))),
        "torch::nn::ConstantPad3d(padding=[1, 2, 1, 2, 1, 2], value=3.5)");
  }
  
  TEST_F(ModulesTest, PrettyPrintNestedModel) {
    struct InnerTestModule : torch::nn::Module {
      InnerTestModule()
          : torch::nn::Module("InnerTestModule"),
            fc(register_module("fc", torch::nn::Linear(3, 4))),
            table(register_module("table", torch::nn::Embedding(10, 2))) {}
  
      torch::nn::Linear fc;
      torch::nn::Embedding table;
    };
  
    struct TestModule : torch::nn::Module {
      TestModule()
          : torch::nn::Module("TestModule"),
            fc(register_module("fc", torch::nn::Linear(4, 5))),
            table(register_module("table", torch::nn::Embedding(EmbeddingOptions(10, 2)))),
            inner(register_module("inner", std::make_shared<InnerTestModule>())) {
      }
  
      torch::nn::Linear fc;
      torch::nn::Embedding table;
      std::shared_ptr<InnerTestModule> inner;
    };
  
    ASSERT_EQ(
        c10::str(TestModule{}),
        "TestModule(\n"
        "  (fc): torch::nn::Linear(in_features=4, out_features=5, bias=true)\n"
        "  (table): torch::nn::Embedding(num_embeddings=10, embedding_dim=2)\n"
        "  (inner): InnerTestModule(\n"
        "    (fc): torch::nn::Linear(in_features=3, out_features=4, bias=true)\n"
        "    (table): torch::nn::Embedding(num_embeddings=10, embedding_dim=2)\n"
        "  )\n"
        ")");
  }
  
  TEST_F(ModulesTest, PrettyPrintELU) {
    ASSERT_EQ(c10::str(ELU()), "torch::nn::ELU(alpha=1)");
    ASSERT_EQ(c10::str(ELU(ELUOptions().alpha(42.42).inplace(true))),
              "torch::nn::ELU(alpha=42.42, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintSELU) {
    ASSERT_EQ(c10::str(SELU()), "torch::nn::SELU()");
    ASSERT_EQ(c10::str(SELU(SELUOptions().inplace(true))),
              "torch::nn::SELU(inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintGLU) {
    ASSERT_EQ(c10::str(GLU()), "torch::nn::GLU(dim=-1)");
    ASSERT_EQ(c10::str(GLU(1)), "torch::nn::GLU(dim=1)");
  }
  
  TEST_F(ModulesTest, PrettyPrintHardshrink) {
    ASSERT_EQ(c10::str(Hardshrink()), "torch::nn::Hardshrink(0.5)");
    ASSERT_EQ(c10::str(Hardshrink(HardshrinkOptions().lambda(42.42))),
              "torch::nn::Hardshrink(42.42)");
  }
  
  TEST_F(ModulesTest, PrettyPrintHardtanh) {
    ASSERT_EQ(c10::str(Hardtanh()),
      "torch::nn::Hardtanh(min_val=-1, max_val=1)");
    ASSERT_EQ(c10::str(Hardtanh(
        HardtanhOptions().min_val(-42.42).max_val(0.42).inplace(true))),
      "torch::nn::Hardtanh(min_val=-42.42, max_val=0.42, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintLeakyReLU) {
    ASSERT_EQ(c10::str(LeakyReLU()),
      "torch::nn::LeakyReLU(negative_slope=0.01)");
    ASSERT_EQ(c10::str(LeakyReLU(
        LeakyReLUOptions().negative_slope(0.42).inplace(true))),
      "torch::nn::LeakyReLU(negative_slope=0.42, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintLogSigmoid) {
    ASSERT_EQ(c10::str(LogSigmoid()), "torch::nn::LogSigmoid()");
  }
  
  TEST_F(ModulesTest, PrettyPrintSoftmax) {
    ASSERT_EQ(c10::str(Softmax(SoftmaxOptions(1))), "torch::nn::Softmax(dim=1)");
  }
  
  TEST_F(ModulesTest, PrettyPrintSoftmin) {
    ASSERT_EQ(c10::str(Softmin(SoftminOptions(1))), "torch::nn::Softmin(dim=1)");
  }
  
  TEST_F(ModulesTest, PrettyPrintLogSoftmax) {
    ASSERT_EQ(c10::str(LogSoftmax(LogSoftmaxOptions(1))),
              "torch::nn::LogSoftmax(dim=1)");
  }
  
  TEST_F(ModulesTest, PrettyPrintSoftmax2d) {
    ASSERT_EQ(c10::str(Softmax2d()), "torch::nn::Softmax2d()");
  }
  
  TEST_F(ModulesTest, PrettyPrintPReLU) {
    ASSERT_EQ(c10::str(PReLU()), "torch::nn::PReLU(num_parameters=1)");
    ASSERT_EQ(c10::str(PReLU(PReLUOptions().num_parameters(42))),
              "torch::nn::PReLU(num_parameters=42)");
  }
  
  TEST_F(ModulesTest, PrettyPrintReLU) {
    ASSERT_EQ(c10::str(ReLU()), "torch::nn::ReLU()");
    ASSERT_EQ(c10::str(ReLU(ReLUOptions().inplace(true))),
      "torch::nn::ReLU(inplace=true)");
    ASSERT_EQ(c10::str(ReLU(/*inplace=*/true)),
      "torch::nn::ReLU(inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintReLU6) {
    ASSERT_EQ(c10::str(ReLU6()), "torch::nn::ReLU6()");
    ASSERT_EQ(c10::str(ReLU6(ReLU6Options().inplace(true))),
      "torch::nn::ReLU6(inplace=true)");
    ASSERT_EQ(c10::str(ReLU6(/*inplace=*/true)),
      "torch::nn::ReLU6(inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintRReLU) {
    ASSERT_EQ(c10::str(RReLU()),
      "torch::nn::RReLU(lower=0.125, upper=0.333333)");
    ASSERT_EQ(c10::str(RReLU(
        RReLUOptions().lower(0.24).upper(0.42).inplace(true))),
      "torch::nn::RReLU(lower=0.24, upper=0.42, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintCELU) {
    ASSERT_EQ(c10::str(CELU()), "torch::nn::CELU(alpha=1)");
    ASSERT_EQ(c10::str(CELU(CELUOptions().alpha(42.42).inplace(true))),
              "torch::nn::CELU(alpha=42.42, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintSigmoid) {
    ASSERT_EQ(c10::str(Sigmoid()), "torch::nn::Sigmoid()");
  }
  
  TEST_F(ModulesTest, PrettyPrintPixelShuffle) {
    ASSERT_EQ(c10::str(PixelShuffle(PixelShuffleOptions(5))),
              "torch::nn::PixelShuffle(upscale_factor=5)");
  }
  
  TEST_F(ModulesTest, PrettyPrintPixelUnshuffle) {
    ASSERT_EQ(
        c10::str(PixelUnshuffle(PixelUnshuffleOptions(5))),
        "torch::nn::PixelUnshuffle(downscale_factor=5)");
  }
  
  TEST_F(ModulesTest, PrettyPrintSoftplus) {
    ASSERT_EQ(c10::str(Softplus()),
      "torch::nn::Softplus(beta=1, threshold=20)");
    ASSERT_EQ(c10::str(Softplus(
        SoftplusOptions().beta(0.24).threshold(42.42))),
      "torch::nn::Softplus(beta=0.24, threshold=42.42)");
  }
  
  TEST_F(ModulesTest, PrettyPrintSoftshrink) {
    ASSERT_EQ(c10::str(Softshrink()), "torch::nn::Softshrink(0.5)");
    ASSERT_EQ(c10::str(Softshrink(SoftshrinkOptions(42.42))),
              "torch::nn::Softshrink(42.42)");
  }
  
  TEST_F(ModulesTest, PrettyPrintSoftsign) {
    ASSERT_EQ(c10::str(Softsign()), "torch::nn::Softsign()");
  }
  
  TEST_F(ModulesTest, PrettyPrintTanh) {
    ASSERT_EQ(c10::str(Tanh()), "torch::nn::Tanh()");
  }
  
  TEST_F(ModulesTest, PrettyPrintTanhshrink) {
    ASSERT_EQ(c10::str(Tanhshrink()), "torch::nn::Tanhshrink()");
  }
  
  TEST_F(ModulesTest, PrettyPrintThreshold) {
    ASSERT_EQ(c10::str(Threshold(24.24, 42.42)),
      "torch::nn::Threshold(threshold=24.24, value=42.42)");
    ASSERT_EQ(c10::str(Threshold(
        ThresholdOptions(42.42, 24.24).inplace(true))),
      "torch::nn::Threshold(threshold=42.42, value=24.24, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintCTCLoss) {
    ASSERT_EQ(c10::str(CTCLoss()), "torch::nn::CTCLoss()");
    ASSERT_EQ(c10::str(CTCLoss(
      CTCLossOptions().blank(42).zero_infinity(false)
        .reduction(torch::kSum))), "torch::nn::CTCLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrintPoissonNLLLoss) {
    ASSERT_EQ(c10::str(PoissonNLLLoss()), "torch::nn::PoissonNLLLoss()");
    ASSERT_EQ(c10::str(PoissonNLLLoss(
      PoissonNLLLossOptions().log_input(false).full(true).eps(0.42)
      .reduction(torch::kSum))),
      "torch::nn::PoissonNLLLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrintMarginRankingLoss) {
    ASSERT_EQ(c10::str(MarginRankingLoss()), "torch::nn::MarginRankingLoss()");
    ASSERT_EQ(c10::str(MarginRankingLoss(
      MarginRankingLossOptions().margin(0.5).reduction(torch::kSum))),
      "torch::nn::MarginRankingLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrintCrossMapLRN2d) {
    ASSERT_EQ(c10::str(CrossMapLRN2d(4)),
      "torch::nn::CrossMapLRN2d(4, alpha=0.0001, beta=0.75, k=1)");
    ASSERT_EQ(c10::str(CrossMapLRN2d(CrossMapLRN2dOptions(3).alpha(1e-5).beta(0.1).k(10))),
      "torch::nn::CrossMapLRN2d(3, alpha=1e-05, beta=0.1, k=10)");
  }
  
  TEST_F(ModulesTest, PrettyPrintAlphaDropout) {
    ASSERT_EQ(c10::str(AlphaDropout()),
      "torch::nn::AlphaDropout(p=0.5, inplace=false)");
    ASSERT_EQ(c10::str(AlphaDropout(AlphaDropoutOptions(0.2))),
      "torch::nn::AlphaDropout(p=0.2, inplace=false)");
    ASSERT_EQ(c10::str(AlphaDropout(AlphaDropoutOptions(0.2).inplace(true))),
      "torch::nn::AlphaDropout(p=0.2, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintFeatureAlphaDropout) {
    ASSERT_EQ(c10::str(FeatureAlphaDropout()),
      "torch::nn::FeatureAlphaDropout(p=0.5, inplace=false)");
    ASSERT_EQ(c10::str(FeatureAlphaDropout(FeatureAlphaDropoutOptions(0.2))),
      "torch::nn::FeatureAlphaDropout(p=0.2, inplace=false)");
    ASSERT_EQ(c10::str(FeatureAlphaDropout(FeatureAlphaDropoutOptions(0.2).inplace(true))),
      "torch::nn::FeatureAlphaDropout(p=0.2, inplace=true)");
  }
  
  TEST_F(ModulesTest, PrettyPrintBCEWithLogitsLoss) {
    ASSERT_EQ(c10::str(BCEWithLogitsLoss()), "torch::nn::BCEWithLogitsLoss()");
    ASSERT_EQ(c10::str(BCEWithLogitsLoss(
      BCEWithLogitsLossOptions()
      .weight(torch::ones({3, 3}))
      .pos_weight(torch::ones({3, 3}))
      .reduction(torch::kSum))),
      "torch::nn::BCEWithLogitsLoss()");
  }
  
  TEST_F(ModulesTest, PrettyPrintMultiheadAttention) {
    ASSERT_EQ(c10::str(MultiheadAttention(20, 10)),
      "torch::nn::MultiheadAttention(\n  (out_proj): torch::nn::Linear(in_features=20, out_features=20, bias=true)\n)");
    ASSERT_EQ(c10::str(MultiheadAttention(MultiheadAttentionOptions(20, 10).bias(false))),
      "torch::nn::MultiheadAttention(\n  (out_proj): torch::nn::Linear(in_features=20, out_features=20, bias=false)\n)");
  }
  
  TEST_F(ModulesTest, PrettyPrintRNNCell) {
    ASSERT_EQ(c10::str(RNNCell(20, 10)),
      "torch::nn::RNNCell(20, 10)");
    ASSERT_EQ(c10::str(RNNCell(RNNCellOptions(20, 10).bias(false).nonlinearity(torch::kTanh))),
      "torch::nn::RNNCell(20, 10, bias=false)");
    ASSERT_EQ(c10::str(RNNCell(RNNCellOptions(20, 10).bias(false).nonlinearity(torch::kReLU))),
      "torch::nn::RNNCell(20, 10, bias=false, nonlinearity=kReLU)");
  }
  
  TEST_F(ModulesTest, PrettyPrintLSTMCell) {
    ASSERT_EQ(c10::str(LSTMCell(20, 10)),
      "torch::nn::LSTMCell(20, 10)");
    ASSERT_EQ(c10::str(LSTMCell(LSTMCellOptions(20, 10).bias(false))),
      "torch::nn::LSTMCell(20, 10, bias=false)");
  }
  
  TEST_F(ModulesTest, PrettyPrintGRUCell) {
    ASSERT_EQ(c10::str(GRUCell(20, 10)),
      "torch::nn::GRUCell(20, 10)");
    ASSERT_EQ(c10::str(GRUCell(GRUCellOptions(20, 10).bias(false))),
      "torch::nn::GRUCell(20, 10, bias=false)");
  }
  
  TEST_F(ModulesTest, PrettyPrintAdaptiveLogSoftmaxWithLoss) {
    {
      AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 4, {2}).div_value(2.));
      ASSERT_EQ(
        c10::str(asfm),
        "torch::nn::AdaptiveLogSoftmaxWithLoss(\n"
        "  (head): torch::nn::Linear(in_features=8, out_features=3, bias=false)\n"
        "  (tail): torch::nn::ModuleList(\n"
        "    (0): torch::nn::Sequential(\n"
        "      (0): torch::nn::Linear(in_features=8, out_features=4, bias=false)\n"
        "      (1): torch::nn::Linear(in_features=4, out_features=2, bias=false)\n"
        "    )\n"
        "  )\n"
        ")");
    }
    {
      AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 10, {4, 8}).div_value(2.).head_bias(true));
      ASSERT_EQ(
        c10::str(asfm),
        "torch::nn::AdaptiveLogSoftmaxWithLoss(\n"
        "  (head): torch::nn::Linear(in_features=8, out_features=6, bias=true)\n"
        "  (tail): torch::nn::ModuleList(\n"
        "    (0): torch::nn::Sequential(\n"
        "      (0): torch::nn::Linear(in_features=8, out_features=4, bias=false)\n"
        "      (1): torch::nn::Linear(in_features=4, out_features=4, bias=false)\n"
        "    )\n"
        "    (1): torch::nn::Sequential(\n"
        "      (0): torch::nn::Linear(in_features=8, out_features=2, bias=false)\n"
        "      (1): torch::nn::Linear(in_features=2, out_features=2, bias=false)\n"
        "    )\n"
        "  )\n"
        ")");
    }
  }
  