//  - 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"

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"

            "//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",

architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for...

        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

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"

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"