final String indexName = resolveIndexName();

            ensureIndexExists(client, indexName);

            for (int i = 0; i < events.size(); i += BATCH_SIZE) {
                final List<LogNotificationEvent> batch = events.subList(i, Math.min(i + BATCH_SIZE, events.size()));
                final BulkRequestBuilder bulkRequest = client.prepareBulk();
                for (final LogNotificationEvent event : batch) {

  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];
  }

# 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

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>

}

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()) {