EXPECT_TRUE(differencer.Compare(node_in, node_out));
}

TEST(CAPI, TestTensorNonScalarBytesAllocateDelete) {
  const int batch_size = 4;
  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }

paging.search.page.max.size=100

# searchlog
searchlog.agg.shard.size=-1
# Request headers to include in search log.
searchlog.request.headers=
# Batch size for search log processing.
searchlog.process.batch_size=100

# Minimum width for HTML images in thumbnails.
thumbnail.html.image.min.width=100
# Minimum height for HTML images in thumbnails.
thumbnail.html.image.min.height=100

     * Get the value for the key 'searchlog.process.batch_size'. <br>
     * The value is, e.g. 100 <br>
     * comment: Batch size for search log processing.
     * @return The value of found property. (NotNull: if not found, exception but basically no way)
     */
    String getSearchlogProcessBatchSize();

    /**
     * Get the value for the key 'searchlog.process.batch_size' as {@link Integer}. <br>

# Release 2.7.0

## 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

		} else if result.Error != nil {
			tx.AddError(result.Error)
		}

		if tx.Error != nil || int(result.RowsAffected) < batchSize {
			break
		}

		if totalSize > 0 {
			if totalSize <= int(rowsAffected) {
				break
			}
			if totalSize/batchSize == batch {
				batchSize = totalSize % batchSize
			}
		}

		// Optimize for-break
		resultsValue := reflect.Indirect(reflect.ValueOf(dest))

     * @param batchCount the number of entries to return
     * @param batchSize the maximum size of the response buffer
     */
    public Trans2FindNext2(final Configuration config, final int sid, final int resumeKey, final String filename, final int batchCount,
            final int batchSize) {
        super(config, SMB_COM_TRANSACTION2, TRANS2_FIND_NEXT2);
        this.sid = sid;

     * @param batchCount the number of entries to return
     * @param batchSize the maximum size of the response buffer
     */
    public Trans2FindFirst2(final Configuration config, final String filename, final String wildcard, final int searchAttributes,
            final int batchCount, final int batchSize) {
        super(config, SMB_COM_TRANSACTION2, TRANS2_FIND_FIRST2);

}

func (c createG[T]) Create(ctx context.Context, r *T) error {
	return c.g.apply(ctx).Create(r).Error
}

func (c createG[T]) CreateInBatches(ctx context.Context, r *[]T, batchSize int) error {
	return c.g.apply(ctx).CreateInBatches(r, batchSize).Error
}

type chainG[T any] struct {
	execG[T]
}

func (c chainG[T]) getInstance() *DB {
	var r T
	return c.g.apply(context.Background()).Model(r).getInstance()

        String[] botNames;
        if (StringUtil.isBlank(value)) {
            botNames = StringUtil.EMPTY_STRINGS;
        } else {
            botNames = value.split(",");
        }

        final int batchSize = fessConfig.getSearchlogProcessBatchSizeAsInteger();

        final List<SearchLog> searchLogList = new ArrayList<>();
        final Map<String, UserInfo> userInfoMap = new HashMap<>();
        while (!queue.isEmpty()) {

    void testConstructorWithVariousParameters(int sid, int resumeKey, String filename, int batchCount, int batchSize) {
        // Test constructor with various parameter combinations
        trans2FindNext2 = new Trans2FindNext2(config, sid, resumeKey, filename, batchCount, batchSize);

        assertNotNull(trans2FindNext2);

        // Verify parameters are written correctly