// 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;

# 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"

  // 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

limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_C_EAGER_CUSTOM_DEVICE_TESTUTIL_H_
#define TENSORFLOW_C_EAGER_CUSTOM_DEVICE_TESTUTIL_H_

// A simple logging device to test custom device registration.
#include <memory>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.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

name: TensorFlow Lite Other Issue description: Use this template to report any
issue in TensorFlow Lite that is not about Converters, Play Services or Ops
body: - type: dropdown id: issue-type attributes: label: Issue Type description:
What type of issue would you like to report? multiple: false options: - Bug -
Build/Install - Performance - Support - Feature Request - Documentation Feature
Request - Documentation Bug - Others validations: required: true - type:

# tensorflow/python:_pywrap_parallel_device.
filegroup(
    name = "lib_headers",
    srcs = ["parallel_device_lib.h"],
)

filegroup(
    name = "lib_sources",
    srcs = ["parallel_device_lib.cc"],
)

filegroup(
    name = "device_headers",
    srcs = ["parallel_device.h"],
)

filegroup(
    name = "device_sources",
    srcs = ["parallel_device.cc"],
)

filegroup(
    name = "headers",

};
using OwnedTFBuffer = std::unique_ptr<TF_Buffer, TFBufferDeleter>;

class CGraphOptimizer : public CustomGraphOptimizer {
 public:
  explicit CGraphOptimizer(TP_Optimizer optimizer, const char* device_type)
      : optimizer_(optimizer), device_type_(device_type) {
    if (optimizer.create_func != nullptr) {
      c_optimizer_ = (*optimizer_.create_func)();
    } else {
      c_optimizer_ = nullptr;
    }
  }

                                    Tensor* tensor);

absl::StatusOr<xla::PjRtCApiClient*> GetPjRtCApiClient(
    const DeviceType& device_type);

absl::Status ResetPjRtClient(const DeviceType& device_type);

}  // namespace tensorflow