TF_ASSERT_OK(OpRegistry::Global()->LookUpOpDef("Add", &expected_op_def));
  string expected_serialized;
  expected_op_def->SerializeToString(&expected_serialized);
  string actual_string(reinterpret_cast<const char*>(buffer->data),
                       buffer->length);
  EXPECT_EQ(expected_serialized, actual_string);

  TF_GraphGetOpDef(graph, "MyFakeOp", buffer, status);

                          const void** buf, size_t* len);

// TODO(josh11b,mrry): Change Session to be able to use a Graph*
// directly, instead of requiring us to serialize to a GraphDef and
// call Session::Extend().
bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) {
  if (session->graph != nullptr) {

// Add attributes in `attrs` to `op`.
//
// Does not overwrite or update existing attributes, but adds new ones.
TF_CAPI_EXPORT extern void TFE_OpAddAttrs(TFE_Op* op, const TFE_OpAttrs* attrs);

// Serialize `attrs` as a tensorflow::NameAttrList protocol buffer (into `buf`),
// containing the op name and a map of its attributes.
TF_CAPI_EXPORT extern void TFE_OpAttrsSerialize(const TFE_OpAttrs* attrs,

    `tf.name_scope` and `tf.variable_scope`. The new argument order of
    `tf.variable_scope` is incompatible with previous versions.
*   Introducing `core/util/tensor_bundle` module: a module to efficiently
    serialize/deserialize tensors to disk. Will be used in TF's new checkpoint
    format.
*   Added tf.svd for computing the singular value decomposition (SVD) of dense
    matrices or batches of matrices (CPU only).

  TFE_Context* ctx = TFE_NewContext(opts, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_ContextSetServerDefWithTimeout(ctx, 0, serialized_server_def.data(),
                                     serialized_server_def.size(),
                                     init_timeout_in_ms, status,
                                     /*clear_existing_contexts=*/false);

  }
  return nullptr;
}

void TFE_ContextAddFunctionDef(TFE_Context* ctx,
                               const char* serialized_function_def, size_t size,
                               TF_Status* status) {
  tensorflow::FunctionDef function_def;
  if (!function_def.ParseFromArray(serialized_function_def, size)) {
    status->status =
        tensorflow::errors::InvalidArgument("Invalid FunctionDef proto");
    return;
  }

#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

class FunctionRecord;

// A traced function: this hides the complexity of converting the serialized
// representation between various supported formats e.g. FunctionDef and Mlir
// function.
class AbstractFunction : public core::RefCounted {
 protected:
  enum AbstractFunctionKind { kGraph, kMlir };

  TFE_Context* ctx = TFE_NewContext(opts, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  const char task0_name[] = "/job:localhost/replica:0/task:0/device:CPU:0";