ASSERT_EQ(errors::OK, status_.code()) << status_.message();

  ASSERT_NO_FATAL_FAILURE(CompareNumericalAndAutodiffGradients(
      ExpModel, BuildGradModel(ExpModel, registry_),
      immediate_execution_ctx_.get(), {x.get()}, UseFunction()));
}

TEST_P(CppGradients, TestMatMulGrad) {
  // TODO(vnvo2409): Figure out why `gradient_checker` does not work very
  // well with `MatMul` and remove `TestMatMul*` in

        immediate_execution_ctx_.get(), X_vals, X_dims, 2, &X_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    X.reset(X_raw);
  }

  ASSERT_NO_FATAL_FAILURE(CompareNumericalAndAutodiffGradients(
      ReluModel, ReluGradModel, immediate_execution_ctx_.get(), {X.get()},
      UseFunction()));

  // Mathematically, Relu isn't differentiable at `0`. So `gradient_checker`

    ::tensorflow::Status _s(__VA_ARGS__);                         \
    if (!TF_PREDICT_TRUE(_s.ok())) {                              \
      ::tensorflow::Set_TF_Status_from_Status(C_STATUS, _s);      \
      TF_OpKernelContext_Failure(CTX, C_STATUS);                  \
      TF_DeleteStatus(C_STATUS);                                  \
      return;                                                     \

      ADD_FAILURE() << i << " -- " << TF_Message(status.get());
      continue;
    }
    auto tag = tensorflow::strings::StrCat("Device #", i, " (", name, ")");
    // Copy to device
    TFE_TensorHandle* hdevice =
        TFE_TensorHandleCopyToDevice(hcpu, ctx, name.c_str(), status.get());
    if (TF_GetCode(status.get()) != TF_OK) {
      ADD_FAILURE() << tag << " -- " << TF_Message(status.get());

  TFE_CancellationManager* c_mgr = TFE_NewCancellationManager();
  EXPECT_FALSE(TFE_CancellationManagerIsCancelled(c_mgr));

  TFE_CancelCallback callback1;
  callback1.callback = [](void* context) {
    ADD_FAILURE() << "Callback1 should be deregistered.";
  };
  TFE_CancellationToken token1 = TFE_CancellationManagerGetToken(c_mgr);
  EXPECT_TRUE(TFE_CancellationManagerRegisterCallback(c_mgr, token1, &callback1,

      EXPECT_FALSE(found_scalar_const);
      found_scalar_const = true;
    } else if (IsAddN(n, 2)) {
      EXPECT_FALSE(found_add);
      found_add = true;
    } else {
      ADD_FAILURE() << "Unexpected NodeDef: " << n.DebugString();
    }
  }
  EXPECT_TRUE(found_placeholder);
  EXPECT_TRUE(found_scalar_const);
  EXPECT_TRUE(found_add);

  // Add another oper to the graph.

namespace tensorflow {
namespace {

using ::tensorflow::error::Code;

// As we need to test multiple URI schemes we need a parameterized test.
// Furthermore, since each test creates and deletes files, we will use the same
// fixture to create new directories in `SetUp`. Each directory will reside in
// `::testing::TempDir()`, will use a RNG component and the test name. This
// ensures that two consecutive runs are unlikely to clash.

        !DeviceNameUtils::IsSameAddressSpace(
            underlying_devices_[component_index], underlying_devices_[0])) {
      // Device names are from different address spaces, or we can't figure out
      // whether they are, so we'll fully-qualify everything.
      return underlying_devices_;
    }
  }
  std::vector<std::string> local_names;
  local_names.reserve(underlying_devices_.size());

      // Note: Needs to be zeros rather than ones since there's currently no
      // ones_like for variants.
      aid = output_tensor.ZerosLike();
    } else {
      // TODO(allenl): Figure out why using zeros_like everywhere causes issues
      // for some gradient functions and if there's another way to work around
      // it (e.g. conds instead of ifs). The value shouldn't really matter.

- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

- Model produces wrong results and/or has lesser accuracy.