}

void TFE_ContextGetFunctionDef(TFE_Context* ctx, const char* function_name,
                               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;
  }

                         status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  const char* function_name = "test_reduce_mul";
  RegisterCollectiveMulFunction(context.get(), function_name, 2, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  TensorHandlePtr value_one(FloatTensorHandle(7., status.get()));

bool GetAttrValue(TF_Operation* oper, const char* attr_name,
                  tensorflow::AttrValue* attr_value, TF_Status* s);

// Returns a sorted vector of std::pair<function_name, gradient_func> from
// graph_def.library().gradient()
std::vector<std::pair<string, string>> GetGradDefs(
    const tensorflow::GraphDef& graph_def);

// Returns a sorted vector of names contained in `grad_def`

                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());
  status->status = g->graph.AddGradientDef(std::move(gdef));
}

int TF_GraphNumFunctions(TF_Graph* g) {

                                                     const char* function_name,
                                                     TF_Buffer* buf,
                                                     TF_Status* status);

// Get GraphDebugInfo containing stack traces mapping to node names
TF_CAPI_EXPORT extern void TFE_ContextGetGraphDebugInfo(
    TFE_Context* ctx, const char* function_name, TF_Buffer* buf,
    TF_Status* status);

    return 0;
  }
  return tensor->TotalBytes();
}

TFE_Op* TFE_NewOp(TFE_Context* ctx, const char* op_or_function_name,
                  TF_Status* status) {
  tensorflow::ImmediateExecutionOperation* new_op =
      tensorflow::unwrap(ctx)->CreateOperation();
  status->status = new_op->Reset(op_or_function_name, nullptr);
  if (!status->status.ok()) {
    new_op->Release();
    new_op = nullptr;
  }

//     the additional sanity checks there seem unnecessary;
typedef struct TFE_Op TFE_Op;

TF_CAPI_EXPORT extern TFE_Op* TFE_NewOp(TFE_Context* ctx,
                                        const char* op_or_function_name,
                                        TF_Status* status);
TF_CAPI_EXPORT extern void TFE_DeleteOp(TFE_Op* op);

// Returns the op or function name `op` will execute.
//

 public:
  FunctionErrorInjectionPass(string error_node, string error_device)
      : error_node_(error_node), error_device_(error_device) {}
  tensorflow::Status Run(const std::string& function_name,
                         const tensorflow::DeviceSet& device_set,
                         const tensorflow::ConfigProto& config_proto,
                         const FunctionOptions& function_options,

    const tensorflow::GraphDef& graph_def) {
  std::vector<std::pair<string, string>> grads;
  for (const tensorflow::GradientDef& grad : graph_def.library().gradient()) {
    grads.emplace_back(grad.function_name(), grad.gradient_func());
  }
  std::sort(grads.begin(), grads.end());
  return grads;
}

std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {
  std::vector<string> names;

typealias ParameterNamesSupplier = (String) -> List<String>?


private
fun ParameterNamesSupplier.parameterNamesFor(typeName: String, functionName: String, parameterTypeNames: List<String>): List<String>? =
    this("$typeName.$functionName(${parameterTypeNames.joinToString(",")})")


/**
 * Provides [ApiType] instances by Kotlin source name from a class path.
 *