#ifdef __cplusplus
// A workaround to ease conversion to and from numpy objects and
// TFE_TensorHandle's.
//
// TODO(ashankar): Figure out an alternative scheme that precludes the need for
// these API-boundary breaking methods.
namespace tensorflow {
class Tensor;
}  // namespace tensorflow

TFE_TensorHandle* TFE_NewTensorHandle(const tensorflow::Tensor& t,
                                      TF_Status* status);
#endif

        checkNotNull(cell.getRowKey(), "row");
        checkNotNull(cell.getColumnKey(), "column");
        checkNotNull(cell.getValue(), "value");
        @SuppressWarnings("unchecked") // all supported methods are covariant
        Cell<R, C, V> immutableCell = (Cell<R, C, V>) cell;
        cells.add(immutableCell);
      } else {
        put(cell.getRowKey(), cell.getColumnKey(), cell.getValue());
      }

     * which are awaiting cleanup.
     */
    volatile int count;

    /**
     * Number of updates that alter the size of the table. This is used during bulk-read methods to
     * make sure they see a consistent snapshot: If modCounts change during a traversal of segments
     * computing size or checking containsValue, then we might have an inconsistent view of state so

 * about crawler operations including timing data, performance metrics, and
 * operational events. It uses an internal cache to maintain statistics
 * objects and provides methods to begin tracking, record events, and
 * finalize statistics collection.
 *
 */
public class CrawlerStatsHelper {

    /**
     * Creates a new instance of CrawlerStatsHelper.
     */

         * response will not read a batched command and therefore
         * the 'received' member of the response object will not
         * be set to true indicating the send and sendTransaction
         * methods that the next part should be sent. This is a
         * very indirect and simple batching control mechanism.
         */

        if (this.andx == null || !getConfig().isUseBatching()

                channelExecutor.shutdownNow();
            }
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
        }
    }

    // Private helper methods

    private int getMaxChannelsFromConfig(Configuration config) {
        // In a real implementation, this would read from configuration
        return 4; // Default to 4 channels
    }

                int expectedSize = copy.size();
                int actualEncoded = copy.encode(buffer, 0);

                // Then
                assertEquals(expectedSize, actualEncoded, "Size and encode methods return different values for " + numChunks + " chunks");
            }
        }

        @Test
        @DisplayName("Should handle large number of chunks")
        void testLargeNumberOfChunks() {

## Breaking Changes

*   `tf.keras`:

    *   The methods `Model.fit()`, `Model.predict()`, and `Model.evaluate()`
        will no longer uprank input data of shape `(batch_size,)` to become
        `(batch_size, 1)`. This enables `Model` subclasses to process scalar
        data in their `train_step()`/`test_step()`/`predict_step()` methods. \
        Note that this change may break certain subclassed models. You can

import jcifs.DialectVersion;
import jcifs.internal.smb2.nego.EncryptionNegotiateContext;
import jcifs.util.SecureKeyManager;

/**
 * Test class for Smb2EncryptionContext.
 * Tests the available public API methods of the encryption context.
 */
@DisplayName("Smb2EncryptionContext Tests")
class Smb2EncryptionContextTest {

    private byte[] testEncryptionKey;
    private byte[] testDecryptionKey;

            summary=summary,
            description=description,
            response_description=response_description,
            responses=responses,
            deprecated=deprecated,
            methods=methods,
            operation_id=operation_id,
            response_model_include=response_model_include,
            response_model_exclude=response_model_exclude,