See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/c/c_api.h"

#include <algorithm>
#include <cstring>
#include <limits>
#include <memory>
#include <optional>
#include <unordered_set>
#include <utility>
#include <vector>

#include "absl/strings/match.h"

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <algorithm>
#include <unordered_map>
#include <unordered_set>
#include <utility>

#include "absl/strings/match.h"
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/tf_buffer_internal.h"

limitations under the License.
==============================================================================*/
#include "tensorflow/c/experimental/filesystem/modular_filesystem.h"

#include <algorithm>
#include <string>
#include <utility>

#include "tensorflow/c/experimental/filesystem/modular_filesystem_registration.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/env.h"

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/c/c_api.h"

#include <algorithm>
#include <cstddef>
#include <iterator>
#include <memory>
#include <vector>

#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/c_test_util.h"
#include "tensorflow/c/tf_buffer.h"

//  - target: dy in dx/dy
//  - source: dx in dx/dy
//  - tensor: one of the many inputs or outputs of an operation
//
// Below here we do the gradient algorithm. It works as follows:
//
// First we filter the tape to just the subset of operations we want to
// differentiate. In the process of doing so we count how many times each Tensor

// "Something" that can be manipulated via calls in the C interface and a C
// struct called "TP_Something".

#include "tensorflow/c/experimental/grappler/grappler.h"

#include <algorithm>
#include <cstddef>
#include <cstring>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "absl/status/status.h"
#include "tensorflow/c/c_api_macros.h"

    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.

==============================================================================*/

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

#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <memory>
#include <string>
#include <utility>
#include <vector>

#include "absl/algorithm/container.h"
#include "absl/memory/memory.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_internal.h"

            "//tensorflow/core/common_runtime/eager:placement_utils",
            "//tensorflow/core/common_runtime/eager:tensor_handle",
            "//tensorflow/core/profiler/lib:traceme",
            "@com_google_absl//absl/algorithm:container",
            "@com_google_absl//absl/types:span",
            "@com_google_absl//absl/types:variant",
        ],
    }) + [
        ":abstract_tensor_handle",
        "//tensorflow/c:c_api_macros_hdrs",

        behavior, set `inject_prefetch=True` in
        `tf.data.experimental.OptimizationOptions`.
    *   Added a new value to `tf.data.Options.autotune.autotune_algorithm`:
        `STAGE_BASED`. If the autotune algorithm is set to `STAGE_BASED`, then
        it runs a new algorithm that can get the same performance with lower
        CPU/memory usage.
    *   Added