"//tensorflow/core:framework",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/common_runtime/eager:context",
    ],
)

cc_library(
    name = "parallel_device_testlib",
    testonly = 1,
    srcs = ["parallel_device_testlib.cc"],
    hdrs = ["parallel_device_testlib.h"],
    deps = [

current build. It utilizes the `_api/v2/api_packages.txt` list of packages from
the local wheel file specified in the `requirements_lock_<python_version>.txt`.

Packages are imported one by one in alphabetical order during runtime.

The test doesn't identify package's order-dependent issues; for instance,
importing "tf.foo" followed by "tf.bar" won't reveal that "tf.bar" depends on
"tf.foo" being imported first.

  TFE_DeleteTensorHandle(retvals[0]);
  TFE_DeleteContext(ctx);
  TF_DeleteStatus(status);
}
TEST(CAPI, Execute_MatMul_CPU_Runtime_Error) {
  Execute_MatMul_CPU_Runtime_Error(false);
}
TEST(CAPI, Execute_MatMul_CPU_Runtime_ErrorAsync) {
  Execute_MatMul_CPU_Runtime_Error(true);
}

void Execute_MatMul_CPU_Type_Error(bool async) {
  TF_Status* status = TF_NewStatus();

        "//tensorflow/core:gpu_runtime",
        "//tensorflow/core/common_runtime/eager:eager_operation",
        "//tensorflow/core/distributed_runtime:remote_device",
        "//tensorflow/core/distributed_runtime:server_lib",
        "//tensorflow/core/distributed_runtime:worker_env",
        "//tensorflow/core/distributed_runtime:worker_interface",
        "//tensorflow/core/distributed_runtime/eager:cluster_function_library_runtime",

        "//tensorflow/core/common_runtime/eager:attr_builder",
        "//tensorflow/core/common_runtime/eager:context",
        "//tensorflow/core/common_runtime/eager:core",
        "//tensorflow/core/common_runtime/eager:eager_operation",
        "//tensorflow/core/common_runtime/pluggable_device:pluggable_device_plugin_init",
        "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
        "//tensorflow/core/platform",

  // Create an operation to perform op execution
  ImmediateExecutionOperation* CreateOperation() override = 0;

  // Returns whether the runtime is backed by TFRT or the legacy TF Eager
  // Runtime. This is necessary to decouple runtime-dependent
  // code that is layered on top of the runtime.
  virtual bool UsesTFRT() = 0;

  // List attributes of available devices

#include <string>

#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/distributed_runtime/server_lib.h"
#include "tensorflow/core/lib/monitoring/collection_registry.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/protobuf.h"

#include "tensorflow/core/common_runtime/copy_tensor.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/common_runtime/eager/attr_builder.h"
#include "tensorflow/core/common_runtime/eager/context.h"
#include "tensorflow/core/common_runtime/eager/custom_device.h"

                "//tensorflow/compiler/tf2tensorrt:op_converter_registry_impl",
                "//tensorflow/core/common_runtime:core_cpu_impl",
                "//tensorflow/core/common_runtime/gpu:gpu_runtime_impl",
                "//tensorflow/core/common_runtime/pluggable_device:pluggable_device_runtime_impl",
                "//tensorflow/core:framework_internal_impl",
                "//tensorflow/core/framework:tensor",

        "tensorflow.distribute.coordinator",
        "tensorflow.distribute.experimental.partitioners",
    ]

  def test_import_runtime(self):
    """Try to import all packages from api packages file one by one."""
    version = "v2"
    failed_packages = []

    logging.info("Try to import packages at runtime...")
    for package_name in self.api_packages_v2:
      # Convert package name to the short version: