#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"
#include "tensorflow/c/tf_status.h"

void RegisterLoggingDevice(TFE_Context* context, const char* name,
                           bool strict_scope_placement, bool* arrived_flag,

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/graph_function.h"

#include <utility>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace tracing {
namespace graph {

#ifndef TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_

#include <memory>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {

// Abstract interface to a context.
//
// This serves as a factory for creating `AbstractOperation`s and for
// registering traced functions.

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

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

#include <string.h>
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/test.h"

TEST(CApiDebug, ScalarCPU) {
  TF_Status* status = TF_NewStatus();

#include "tensorflow/c/eager/parallel_device/parallel_device_lib.h"

#include <string>
#include <utility>

#include "absl/memory/memory.h"
#include "absl/status/status.h"
#include "absl/strings/str_cat.h"
#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"

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

#include "tensorflow/c/eager/c_api_experimental_reader.h"

#include <cstdint>

#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

#include <array>
#include <memory>
#include <string>

#include "absl/log/log.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/parallel_device/parallel_device_lib.h"
#include "tensorflow/c/eager/parallel_device/parallel_device_testlib.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/distributed_runtime/master_env.h"

#ifndef TENSORFLOW_C_EAGER_PARALLEL_DEVICE_PARALLEL_DEVICE_H_
#define TENSORFLOW_C_EAGER_PARALLEL_DEVICE_PARALLEL_DEVICE_H_

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

namespace tensorflow {
namespace parallel_device {

// Allocate a parallel device named `device_name` which forwards operations to

vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference

#include "absl/status/status.h"
#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "absl/types/variant.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/tfe_op_internal.h"
#include "tensorflow/c/safe_ptr.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_tensor.h"