// C API for device. The API is under active development and eventually
// should allow registering a plugin device with TensorFlow.

#ifdef __cplusplus
extern "C" {
#endif

// TF_Device is a C wrapper to the C++ TF Device class. This is to be passed
// through TF_OpKernelContext, and is opaque to plugin.
typedef struct TF_Device TF_Device;

typedef struct TF_VariableInfo TF_VariableInfo;

#include "xla/tsl/c/tsl_status_internal.h"
#include "xla/tsl/distributed_runtime/coordination/coordination_service_agent.h"
#include "tensorflow/core/common_runtime/composite_device.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/eager/eager_operation.h"
#include "tensorflow/core/distributed_runtime/coordination/coordination_service_error_util.h"
#include "tensorflow/core/framework/function.h"

  pip install twine==3.6.0
fi

# Scheduled nightly and release builds upload build artifacts (Pip packages,
# Libtensorflow archives) to GCS buckets. TFCI Mac VMs need to authenticate as
# a service account that has the right permissions to be able to do so.
set +x
if [[ -n "${GOOGLE_APPLICATION_CREDENTIALS:-}" ]]; then
  # Python 3.12 removed the module `imp` which is needed by gcloud CLI so we set

tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

Since any tenants or users providing models, graphs or checkpoints can execute
code in context of the TensorFlow service, it is important to design isolation
mechanisms that prevent unwanted access to the data from other tenants.

Network isolation between different models is also important not only to prevent

  // device if any of their inputs are on that custom device, but custom devices
  // are free to set a bad status in order to require explicit placement.
  void (*execute)(const TFE_Op* op, int* num_outputs,
                  TFE_TensorHandle** outputs, TF_Status* s, void* device_info);

  // Method to delete a device.
  void (*delete_device)(void* device_info);

# pretend the path is a directory.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
TFCI_ARTIFACT_FINAL_GCS_SA_PATH="${KOKORO_KEYSTORE_DIR}/73361_tensorflow_release_binary_uploader_service_account"
TFCI_ARTIFACT_FINAL_GCS_URI="gs://tensorflow/versions/"
TFCI_ARTIFACT_FINAL_PYPI_ARGS="--config-file=$KOKORO_KEYSTORE_DIR/73361_tensorflow_pypirc_using_global_api_token --repository pypi-warehouse"

    *   Added support for cross-trainer data caching in tf.data service. This
        saves computation resources when concurrent training jobs train from the
        same dataset. See
        (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers)
        for more details.
    *   Added `dataset_id` to `tf.data.experimental.service.register_dataset`.

  // Check if the remote task is alive.
  virtual Status CheckRemoteAlive(const std::string& remote_task_name,
                                  bool* is_alive) = 0;

  // Get pointer to the coordination service agent instance.
  virtual tsl::CoordinationServiceAgent* GetCoordinationServiceAgent() = 0;
};
}  // namespace tensorflow

        "//tensorflow/core/distributed_runtime/eager:eager_client",
        "//tensorflow/core/distributed_runtime/rpc:grpc_channel",
        "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
        "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache",
        "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service",

        "@local_xla//xla/service:computation_placer",
        "//tensorflow/core",
        "//tensorflow/core/common_runtime/eager:context",
        "//tensorflow/core/common_runtime/eager:tensor_handle",
        "//tensorflow/core/config:flag_defs",
        "//tensorflow/core/config:flags",
        "//tensorflow/core/data/service:dispatcher_client",
        "//tensorflow/core/data/service:grpc_util",