valid := 0
									for shardIdx, shard := range splitFilled[:k] {
										shardConfig[shardIdx] = shard[offset]
										valid += int(shard[offset])
										if shard[offset] == 0 {
											shards[shardIdx] = shards[shardIdx][:0]
										} else {
											shards[shardIdx] = append(shards[shardIdx][:0], splitData[shardIdx][offset])
										}
									}

    }

    /**
     * Starts a reindex operation with the specified parameters.
     *
     * @param replaceAliases whether to replace aliases after reindexing
     * @param resetDictionaries whether to reset dictionaries during reindexing
     * @param numberOfShards the number of shards for the new index

code is a mathematical algorithm to reconstruct missing or corrupted data. MinIO uses Reed-Solomon code to shard objects into variable data and parity blocks. For example, in a 12 drive setup, an object can be sharded to a variable number of data and parity blocks across all the drives - ranging from six data and six parity blocks to ten data and two parity blocks.

By default, MinIO shards the objects across N/2 data and N/2 parity drives. Though, you can use [storage classes](https://github...

     */
    public void setBulkBufferSize(final int bulkBufferSize) {
        this.bulkBufferSize = bulkBufferSize;
    }

    /**
     * Sets the number of shards for the OpenSearch index.
     *
     * @param numberOfShards The number of shards.
     */
    public void setNumberOfShards(final int numberOfShards) {
        this.numberOfShards = numberOfShards;
    }

    /**

            public void onResponse(final SearchResponse searchResponse) {
                if (searchResponse.getFailedShards() > 0) {
                    deferred.reject(new SuggesterException("Search failure. Failed shards num:" + searchResponse.getFailedShards()));
                } else {
                    deferred.resolve(createResponse(searchResponse));
                }
            }

            @Override

            public void onResponse(final SearchResponse searchResponse) {
                if (searchResponse.getFailedShards() > 0) {
                    deferred.reject(new SuggesterException("Search failure. Failed shards num:" + searchResponse.getFailedShards()));
                } else {
                    deferred.resolve(createResponse(searchResponse));
                }
            }

            @Override

			// In this case, parity == 0 implies that this object version is a
			// delete marker
			readQuorum = N/2 + 1
		}
		if occ < readQuorum {
			// Ignore this parity since we don't have enough shards for read quorum
			continue
		}

		if occ > maxOcc {
			maxOcc = occ
			cparity = parity
		}
	}

	if maxOcc == 0 {
		// Did not found anything useful
		return -1
	}

	// To avoid these problems we must split the work at scale. With 1000 node
	// setup becoming a reality we must try to shard the work properly such as
	// pick 10 nodes that precisely can send those 100 requests the first node
	// in the 10 node shard would coordinate between other 9 shards to get the
	// rest of the `99*9` requests.
	//
	// This essentially splits the workload properly and also allows for network

     *
     * @param index              the index configuration name
     * @param indexName          the actual index name to create
     * @param numberOfShards     the number of primary shards
     * @param autoExpandReplicas the auto expand replicas setting
     * @param uploadConfig       whether to upload configuration files
     * @return true if the index was created successfully, false otherwise
     */

      tensors will be split into shards when the saver writes the checkpoint
      shards to disk. `tf.train.experimental.ShardByTaskPolicy` is the default
      sharding behavior, but `tf.train.experimental.MaxShardSizePolicy` can be
      used to shard the checkpoint with a maximum shard file size. Users with
      advanced use cases can also write their own custom