}
}

/**
 * Determine the number of buckets for the first element in the list
 * when it needs to be split into several smaller pieces.
 *
 * The basic idea is:
 * 1. Make sure the rest elements has at least one bucket.
 * 2. Make sure the "roughSizeOfEachBucket" for the rest elements is smaller than the current "roughSizeOfEachBucket".
 */
private fun <T> determineBucketNumberForLargeElment(
    largestElementSize: Int,

		case "Tiers":
			var zb0002 uint32
			zb0002, err = dc.ReadMapHeader()
			if err != nil {
				err = msgp.WrapError(err, "Tiers")
				return
			}
			if z.Tiers == nil {
				z.Tiers = make(map[string]madmin.TierConfig, zb0002)
			} else if len(z.Tiers) > 0 {
				clear(z.Tiers)
			}
			for zb0002 > 0 {
				zb0002--
				var za0001 string
				za0001, err = dc.ReadString()
				if err != nil {

// in the slices returned.
func hashOrder(key string, cardinality int) []int {
	if cardinality <= 0 {
		// Returns an empty int slice for cardinality < 0.
		return nil
	}

	nums := make([]int, cardinality)
	keyCrc := crc32.Checksum([]byte(key), crc32.IEEETable)

	start := int(keyCrc % uint32(cardinality))
	for i := 1; i <= cardinality; i++ {
		nums[i-1] = 1 + ((start + i) % cardinality)
	}

package sql

import (
	"fmt"
	"strings"
)

// String functions

// String - returns the JSONPath representation
func (e *JSONPath) String() string {
	if len(e.pathString) == 0 {
		parts := make([]string, len(e.PathExpr)+1)
		parts[0] = e.BaseKey.String()
		for i, pe := range e.PathExpr {
			parts[i+1] = pe.String()
		}
		e.pathString = strings.Join(parts, "")
	}
	return e.pathString
}

/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

The tests:

## Code snippets { #code-snippets }

//// tab | Test

  private final ElementOrder<N> nodeOrder;
  private final ElementOrder<E> edgeOrder;

  final MapIteratorCache<N, NetworkConnections<N, E>> nodeConnections;

  // We could make this a Map<E, EndpointPair<N>>. It would make incidentNodes(edge) slightly
  // faster, but also make Networks consume 5 to 20+% (increasing with average degree) more memory.
  final MapIteratorCache<E, N> edgeToReferenceNode; // referenceNode == source if directed

	v := ChecksumInfo{}
	b.ReportAllocs()
	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		v.MarshalMsg(nil)
	}
}

func BenchmarkAppendMsgChecksumInfo(b *testing.B) {
	v := ChecksumInfo{}
	bts := make([]byte, 0, v.Msgsize())
	bts, _ = v.MarshalMsg(bts[0:0])
	b.SetBytes(int64(len(bts)))
	b.ReportAllocs()
	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		bts, _ = v.MarshalMsg(bts[0:0])
	}
}

If you want to secure your API, there are several better things you can do, for example:

* Make sure you have well defined Pydantic models for your request bodies and responses.

You can read it to understand better what has changed.

## Tests { #tests }

Make sure you have [tests](../tutorial/testing.md) for your app and you run them on continuous integration (CI).

This way, you can do the upgrade and make sure everything is still working as expected.

## `bump-pydantic` { #bump-pydantic }