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

#include <gmock/gmock.h>
#include "absl/status/status.h"
#include "tensorflow/c/c_api_experimental.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/parallel_device/parallel_device_testlib.h"
#include "tensorflow/c/eager/tfe_context_internal.h"
#include "tensorflow/c/tf_buffer.h"

#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/tfe_context_internal.h"
#include "tensorflow/c/eager/tfe_op_internal.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/c/tf_buffer_internal.h"
#include "tensorflow/compiler/jit/flags.h"
#include "tensorflow/core/common_runtime/eager/attr_builder.h"

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 {

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"

#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_OPERATION_H_

#include <memory>

#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_operation.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/cancellation.h"
#include "tensorflow/core/framework/device_attributes.pb.h"

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

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

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_tensor.h"

namespace tensorflow {
namespace gradients {

![Admin UI](https://fess.codelibs.org/_images/fess_admin_dashboard.png)

Vous pouvez enregistrer des cibles à explorer dans l'interface d'administration sur les pages de configuration des crawlers (Web, Fichiers, Data Store), puis démarrer manuellement le Crawler sur la [page du Planificateur](https://fess.codelibs.org/14.17/admin/scheduler-guide.html).

## Migration depuis un autre fournisseur de recherche

==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_C_API_TEST_UTIL_H_
#define TENSORFLOW_C_EAGER_C_API_TEST_UTIL_H_

#include <vector>

#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/tstring.h"
#include "tensorflow/core/platform/types.h"