* @since 10.0
 */
@DoNotMock(
    "Use TestingExecutors.sameThreadScheduledExecutor, or wrap a real Executor from "
        + "java.util.concurrent.Executors with MoreExecutors.listeningDecorator")
@GwtIncompatible
public interface ListeningExecutorService extends ExecutorService {
  /**
   * @return a {@code ListenableFuture} representing pending completion of the task
   * @throws RejectedExecutionException {@inheritDoc}
   */
  @Override

import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;

@ExtendWith(MockitoExtension.class)
public class ResponseTest {

    @Mock
    private Response mockResponse;

    @BeforeEach
    void setUp() {
        // Reset mock before each test to ensure clean state

specific ports. Use below commands to allow access to port 9000 ```sh firewall-cmd --get-active-zones ``` This command gets the active zone(s). Now, apply port rules to the relevant zones returned above. For example if the zone is `public`, use ```sh firewall-cmd --zone=public --add-port=9000/tcp --permanent ``` Note that `permanent` makes sure the rules are persistent across firewall start, restart or reload. Finally reload the firewall for changes to take effect. ```sh firewall-cmd --reload ``` ###...

```sh
firewall-cmd --get-active-zones
```

This command gets the active zone(s). Now, apply port rules to the relevant zones returned above. For example if the zone is `public`, use

```sh
firewall-cmd --zone=public --add-port=9000/tcp --permanent
```

> [!NOTE]
> `permanent` makes sure the rules are persistent across firewall start, restart or reload. Finally reload the firewall for changes to take effect.

# These are used to generate the ML wheel version string.
# See the explanation in the file comment of
# @local_xla//third_party/py/python_wheel.bzl.
# The generated version suffix is used in
# third_party/tensorflow/core/public/release_version.h and
# third_party/tensorflow/tools/pip_package/setup.oss.py.tpl
build --repo_env=ML_WHEEL_TYPE="snapshot"
build --repo_env=ML_WHEEL_BUILD_DATE=""
build --repo_env=ML_WHEEL_VERSION_SUFFIX=""

// it under the terms of the GNU Affero General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Affero General Public License for more details.
//

 * License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 *
 * This library is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Lesser General Public License for more details.
 *

// it under the terms of the GNU Affero General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Affero General Public License for more details.
//

// it under the terms of the GNU Affero General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Affero General Public License for more details.
//

  return ret;
}

void TF_MakeInternalErrorStatus(TF_Status* status, const char* errMsg) {
  status->status = tensorflow::errors::Internal(errMsg);
}

struct TF_CheckpointReader : public tensorflow::checkpoint::CheckpointReader {
  using tensorflow::checkpoint::CheckpointReader::CheckpointReader;
  std::vector<std::string> variable_list;
};