package(default_visibility = ["//visibility:public"])

licenses(["notice"])  # Apache 2.0

filegroup(
    name = "model_files",
    srcs = glob(
        [
            "**/*",
        ],
        exclude = [
            "**/BUILD",
            "**/WORKSPACE",
            "**/LICENSE",
            "**/*.zip",
        ],
    ),

//   outputs = tf.function(model)(inputs)
// else:
//   outputs = model(inputs)
Status RunModel(Model model, AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs, bool use_function);

Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx);

// Return a tensor handle with given type, values and dimensions.

void CompareNumericalAndAutodiffGradients(
    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error = 1e-2);

void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,
                      absl::Span<const int64_t> dims, double abs_error = 1e-2);

Model BuildGradModel(Model forward, GradientRegistry registry);

    false

-   type: input id: Cuda attributes: label: CUDA/cuDNN version description:
    placeholder: validations: required: false

-   type: input id: Gpu attributes: label: GPU model and memory description: if
    compiling from source placeholder: validations: required: false

-   type: textarea id: what-happened attributes: label: Current Behaviour?

  cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF
benchmark
_test$
_test.py$
_test_cpu$
_test_cpu.py$
_test_gpu$
_test_gpu.py$
_test_lib$
//tensorflow/cc/saved_model:saved_model_test_files
//tensorflow/cc/saved_model:saved_model_half_plus_two
//tensorflow:no_tensorflow_py_deps
//tensorflow/tools/pip_package:win_pip_package_marker
//tensorflow/core:image_testdata
//tensorflow/core/lib/lmdb:lmdb_testdata

  TF_RETURN_IF_ERROR(ops::SparseSoftmaxCrossEntropyWithLogits(
      ctx, inputs[0], inputs[1], &loss, &backprop,
      "SparseSoftmaxCrossEntropyWithLogits"));
  // `gradient_checker` only works with model that returns only 1 tensor.
  // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the
  // second tensor isn't needed for computing gradient so we could safely drop
  // it.
  outputs[0] = loss;

      RunModel(forward, ctx, inputs, model_outputs, use_function));
  AbstractTensorHandlePtr model_out(model_outputs[0]);

  TF_Tensor* model_out_tensor;
  TF_RETURN_IF_ERROR(GetValue(model_out.get(), &model_out_tensor));
  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();

  SetViaProto(desc_, {});
  FinishAndVerify(desc_, {});
}

TEST(CAPI, SavedModel) {
  // Load the saved model.
  const string saved_model_dir = tensorflow::GetDataDependencyFilepath(
      tensorflow::io::JoinPath("tensorflow", "cc", "saved_model", "testdata",
                               "half_plus_two", "00000123"));
  TF_SessionOptions* opt = TF_NewSessionOptions();

Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.

Also, please include a link to a GraphDef or the model if possible.

**Any other info / logs**

Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files

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