limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_ABSTRACT_FUNCTION_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_FUNCTION_H_

#include "absl/status/statusor.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/platform/intrusive_ptr.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/status.h"

}

// Runs `model` maybe wrapped in a function.
Status RunModel(Model model, AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs, bool use_function) {
  if (use_function) {
    const char* fn_name = "test_fn";
    core::RefCountPtr<AbstractFunction> scoped_func;
    // Returning null tensors from a tf.function is not supported, so we keep

                               TF_Buffer* buf, TF_Status* status) {
  auto* function_def = tensorflow::unwrap(ctx)->FindFunctionDef(function_name);
  if (function_def == nullptr) {
    status->status = tensorflow::errors::NotFound(
        "Unable to find FunctionDef with name: ", function_name);
    return;
  }
  string str = function_def->SerializeAsString();

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/graph_function.h"

#include <utility>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace tracing {
namespace graph {

        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "graph_function",
    srcs = ["graph_function.cc"],
    hdrs = [
        "graph_function.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_function",
        "//tensorflow/core:framework",
        "//tensorflow/core/platform:refcount",

    x.reset(x_raw);
  }

  Status s = RunModel(TestScalarShape, ctx.get(),
                      /*inputs=*/{x.get()},
                      /*outputs=*/{},
                      /*use_function=*/UseFunction());
  ASSERT_EQ(errors::OK, s.code()) << s.message();
}

// Checks that inputs[0] is a matrix with shape 2x4.
Status TestTensorShape2x4(AbstractContext* ctx,

            ] + tf_additional_binary_deps() +
            # TODO(b/259305727): Remove this select and include captured_function in macos builds.
            select({
                "//tensorflow:macos": [],
                "//conditions:default": [
                    "//tensorflow/core/data:captured_function",
                ],
            }),
    soversion = VERSION,
    static_deps = PACKAGE_STATIC_DEPS,

  // (`output_tensors`).
  //
  // If provided, a non-null `forward_function` will be used instead of the
  // backward function (`backward_function_getter` /
  // `backward_function_deleter`) to compute jvps for this operation. If
  // `forward_function` is null, a GradientTape is used on the backward function
  // to compute the jvp, which will waste computation when executing eagerly.
  //

  for (const Node* n : fn_body->graph.nodes()) {
    stack_traces[n->name()] = n->GetStackTrace();
  }

  TF_Function* tf_function = new TF_Function();
  tf_function->record = new tensorflow::FunctionRecord(
      std::move(fdef), std::move(stack_traces), false);

  return tf_function;
}

TF_Function* TF_GraphToFunction(const TF_Graph* fn_body, const char* fn_name,

}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {
  const auto& debug_str = DebugString(func->record->fdef());
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

// On success, returns a set of TF_Function instances from `text_proto` of
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//