#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/platform/stringpiece.h"
#include "tensorflow/core/platform/tstring.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saved_model.pb.h"
#include "tensorflow/core/protobuf/saved_object_graph.pb.h"
#include "tensorflow/core/protobuf/trackable_object_graph.pb.h"

namespace tensorflow {

                renderExecutions();
            }

            private void renderExecutions() {
                h3().text("Executions for commit " + Git.current().getCommitId()).end();
                getGraphs(testHistory).forEach(graph -> graph.render(this));
            }
        };
    }
    // @formatter:on

    private static class Chart {
        private String field;
        private String label;

  metagraph_def.ParseFromArray(metagraph->data, metagraph->length);
  TF_DeleteBuffer(metagraph);

  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  CSession csession(session);

  // Retrieve the regression signature from meta graph def.
  const auto signature_def_map = metagraph_def.signature_def();
  const auto signature_def = signature_def_map.at("regress_x_to_y");

  with ops.Graph().as_default(), session.Session() as sess:
    meta_graph: meta_graph_pb2.MetaGraphDef = loader_impl.load(
        sess, tags, export_dir=model_dir
    )

    for signature_key, repr_ds in representative_dataset_map.items():
      sig_def = meta_graph.signature_def[signature_key]

      try:
        _run_function_for_calibration_graph_mode(

#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saver.pb.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace mlir::quant::stablehlo {
namespace {

#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace tensorflow {
namespace {

constexpr char kTestDataPbTxt[] =
    "cc/saved_model/testdata/half_plus_two_pbtxt/00000123";
constexpr char kTestDataMainOp[] =

  *input = {oper, 0};
  return true;
}

// Copies `src_graph` into `dst_graph`. Any node in `src_graph` with input
// `src_inputs[i]` will have that input replaced with `dst_inputs[i]`.  `prefix`
// will be prepended to copied node names. `control_deps` are nodes in
// `dst_graph` that the copied `src_graph` nodes will have control dependencies

#include "tensorflow/core/platform/fingerprint.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/protobuf/fingerprint.pb.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saved_model.pb.h"
#include "tensorflow/core/protobuf/saved_object_graph.pb.h"
#include "tensorflow/core/util/tensor_bundle/naming.h"
#include "tensorflow/tools/proto_splitter/cc/util.h"

  TFE_DeleteContext(ctx);
}

// This tests running the "serving_default" SignatureDefFunction from the
// VarsAndArithmeticObjectGraph savedmodel. Here's what the signature_defs
// protobuf in the metagraph looks like:
// signature_def: {
//   key  : "serving_default"
//   value: {
//     inputs: {
//       key  : "a"
//       value: {
//         name : "serving_default_a:0"
//         dtype: DT_FLOAT

  // names are prefixed with the cluster names.  This can help visualizing the
  // clustering decisions on TensorBoard.
  Graph new_graph(graph_->op_registry());
  CopyGraph(*graph_, &new_graph);

  for (Node* n : new_graph.nodes()) {
    if (std::optional<absl::string_view> cluster_name =
            GetXlaClusterForNode(*n)) {
      n->set_name(absl::StrCat(*cluster_name, "/", n->name()));