}
									// Update to new config and add current
									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 {

				fmt.Fprintf(os.Stderr, "%v: error on self-test [d:%d,p:%d]: want %#v, got %#v\n", algo, conf[0], conf[1], a, b)
				ok = false
				continue
			}
			// Delete first shard and reconstruct...
			first := encoded[0]
			encoded[0] = nil
			failOnErr(e.DecodeDataBlocks(encoded))
			if a, b := first, encoded[0]; !bytes.Equal(a, b) {

## What is Erasure Code?

	shardSize := int64(1024 * 1024)
	shard := make([]byte, shardSize)
	w := newStreamingBitrotWriter(storage, "", volName, fileName, size, algo, shardSize)
	reader := bytes.NewReader(data)
	for {
		// Using io.Copy instead of this loop will not work for us as io.Copy
		// will use bytes.Reader.WriteTo() which will not do shardSize'ed writes
		// causing error.
		n, err := reader.Read(shard)
		w.Write(shard[:n])

	// 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.
	//

			disks := er.getDisks()
			distribution := hashOrder(pathJoin(bucket, object), nDisks)
			shuffledDisks := shuffleDisks(disks, distribution)

			// remove last data shard
			err = removeAll(pathJoin(shuffledDisks[11].String(), bucket, object))
			if err != nil {
				t.Fatalf("Failed to delete a file - %v", err)
			}
			_, err = obj.HealObject(ctx, bucket, object, "", madmin.HealOpts{

      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
      `tf.train.experimental.ShardingCallback`s.

* `tf.train.CheckpointOptions`

            return;
        case 2:
            hard = true;
        case 3: /* connected - go ahead and disconnect */
            if (response_map.size() != 0 && !hard) {
                break; /* outstanding requests */
            }
            try {
                doDisconnect(hard);
            } catch (final IOException ioe0) {
                ioe = ioe0;
            }

- [Use `mc` with MinIO Server](https://docs.min.io/community/minio-object-store/reference/minio-mc.html#quickstart)

## Set bucket quota configuration

### Set a hard quota of 1GB for a bucket `mybucket` on MinIO object storage

```sh
mc admin bucket quota myminio/mybucket --hard 1gb
```

### Verify the quota configured on `mybucket` on MinIO

```sh
mc admin bucket quota myminio/mybucket
```

    /**
     * Disconnect the transport
     *
     * @param hard if true, disconnect immediately without waiting for outstanding requests
     * @return whether connection was in use
     * @throws IOException if an I/O error occurs during disconnection
     */
    public synchronized boolean disconnect(final boolean hard) throws IOException {
        return disconnect(hard, true);
    }

    /**
     * Disconnect the transport