}
									// 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 {

	StandardEnv = "MINIO_STORAGE_CLASS_STANDARD"
	// Optimize storage class environment variable
	OptimizeEnv = "MINIO_STORAGE_CLASS_OPTIMIZE"
	// Inline block indicates the size of the shard
	// that is considered for inlining, remember this
	// shard value is the value per drive shard it
	// will vary based on the parity that is configured
	// for the STANDARD storage_class.
	// inlining means data and metadata are written

				// Reading first time on this disk, hence the buffer needs to be allocated.
				// Subsequent reads will reuse this buffer.
				p.buf[bufIdx] = make([]byte, p.shardSize)
			}
			// For the last shard, the shardsize might be less than previous shard sizes.
			// Hence the following statement ensures that the buffer size is reset to the right size.
			p.buf[bufIdx] = p.buf[bufIdx][:p.shardSize]
			n, err := rr.ReadAt(p.buf[bufIdx], p.offset)

	if len(responses) == 0 {
		return &retval, nil
	}

	for _, response := range responses {
		// Combine all the shards as one, even if that means losing information about
		// the control plane version from each shard.
		retval.ControlPlane = response.ControlPlane
		retval.Resources = append(retval.Resources, response.Resources...)
	}

	return &retval, nil
}

func makeSan(istioNamespace, revision string) string {

	// 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.
	//

page.searchlog.max.fetch.size=10000

page.searchlist.track.total.hits=true

# search page
paging.search.page.start=0
paging.search.page.size=10
paging.search.page.max.size=100

searchlog.agg.shard.size=-1
searchlog.request.headers=
searchlog.process.batch_size=100

thumbnail.html.image.min.width=100
thumbnail.html.image.min.height=100
thumbnail.html.image.max.aspect.ratio=3.0

    /**
     * Get the value for the key 'searchlog.agg.shard.size'. <br>
     * The value is, e.g. -1 <br>
     * @return The value of found property. (NotNull: if not found, exception but basically no way)
     */
    String getSearchlogAggShardSize();

    /**
     * Get the value for the key 'searchlog.agg.shard.size' as {@link Integer}. <br>
     * The value is, e.g. -1 <br>

		}
		if osErrToFileErr(err) == errFileNotFound {
			resp = checkPartFileNotFound
		}
		return
	}
	if st.Mode().IsDir() {
		resp = checkPartFileNotFound
		return
	}
	// Check if shard is truncated.
	if st.Size() < expectedSize {
		resp = checkPartFileCorrupt
		return
	}
	return checkPartSuccess
}

// CheckParts check if path has necessary parts available.

      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`

  <dependencies>
    <dependency>
      <groupId>test</groupId>
      <artifactId>a</artifactId>
      <version>0.2</version>
      <scope>system</scope>
      <systemPath>should-use-variables-and-not-hard-code-this-path</systemPath>
    </dependency>
    <dependency>
      <groupId>test</groupId>
      <artifactId>b</artifactId>
      <version>0.1</version>
      <scope>system</scope>