pkg runtime, func MutexProfile([]BlockProfileRecord) (int, bool)
pkg runtime, func SetMutexProfileFraction(int) int
pkg runtime, type MemStats struct, NumForcedGC uint32
pkg sort, func Slice(interface{}, func(int, int) bool)
pkg sort, func SliceIsSorted(interface{}, func(int, int) bool) bool
pkg sort, func SliceStable(interface{}, func(int, int) bool)
pkg syscall (linux-arm-cgo), func TimevalToNsec(Timeval) int64

		return
	}
	disks := setup.disks
	erasure, err := NewErasure(t.Context(), dataBlocks, parityBlocks, blockSize)
	if err != nil {
		t.Fatalf("failed to create ErasureStorage: %v", err)
	}
	// Prepare a slice of 5MiB with random data.
	data := make([]byte, 5*humanize.MiByte)
	length := int64(len(data))
	_, err = rand.Read(data)
	if err != nil {
		t.Fatal(err)
	}

	writers := make([]io.Writer, len(disks))

	"io"
	"io/fs"
	"maps"
	"os"
	"path"
	"slices"
	"sort"
	"strings"
	"testing"
	"testing/fstest"
	"testing/iotest"
	"time"
)

func bytediff(a, b []byte) string {
	const (
		uniqueA  = "-  "
		uniqueB  = "+  "
		identity = "   "
	)
	var ss []string
	sa := strings.Split(strings.TrimSpace(hex.Dump(a)), "\n")
	sb := strings.Split(strings.TrimSpace(hex.Dump(b)), "\n")

* **Deep Learning**: este es un subcampo de Machine Learning, por lo tanto, se aplica lo mismo. Es solo que no hay una sola hoja de cálculo de números para multiplicar, sino un enorme conjunto de ellas, y en muchos casos, usas un procesador especial para construir y / o usar esos modelos.

			allowPubKeys = filterAlgos(arg, strings.Split(tokens[1], ","), supportedPubKeyAuthAlgos)
		case "kex-algos":
			allowKexAlgos = filterAlgos(arg, strings.Split(tokens[1], ","), supportedKexAlgos)
		case "cipher-algos":
			allowCiphers = filterAlgos(arg, strings.Split(tokens[1], ","), supportedCiphers)
		case "mac-algos":
			allowMACs = filterAlgos(arg, strings.Split(tokens[1], ","), supportedMACs)
		case "trusted-user-ca-key":

	if objectAPI == nil {
		return
	}

	cfg := globalServerConfig.Clone()
	vars := mux.Vars(r)
	key := vars["key"]

	var subSys, target string
	{
		ws := strings.SplitN(key, madmin.SubSystemSeparator, 2)
		subSys = ws[0]
		if len(ws) == 2 {
			if ws[1] == "" {
				target = madmin.Default
			} else {
				target = ws[1]
			}
		}
	}

Isso também se aplica ao `response_model_by_alias` que funciona de forma semelhante.

///

{* ../../docs_src/response_model/tutorial005_py310.py hl[29,35] *}

/// tip | Dica

		return r, err
	}
	for p := range strings.SplitSeq(s, ",") {
		if p == "" {
			continue
		}
		slc := strings.Split(p, "=")
		if len(slc) != 2 {
			return r, errInvalidReplicateDecisionFormat
		}
		tgtStr := strings.TrimSuffix(strings.TrimPrefix(slc[1], `"`), `"`)
		tgt := strings.Split(tgtStr, ";")
		if len(tgt) != 4 {
			return r, errInvalidReplicateDecisionFormat
		}

  TF_OperationDescription* desc = TF_NewOperation(graph, "Split", name);
  TF_AddInput(desc, {zero, 0});
  TF_AddInput(desc, {input, 0});
  TF_SetAttrInt(desc, "num_split", 3);
  TF_SetAttrType(desc, "T", TF_INT32);
  // Set device to CPU since there is no version of split for int32 on GPU
  // TODO(iga): Convert all these helpers and tests to use floats because

	})
	if err != nil {
		c.Fatalf("GetLDAPPolicyEntities should not fail: %v", err)
	}
	{
		// Check that the mapping we created exists.
		idx := slices.IndexFunc(policyResult.PolicyMappings, func(e madmin.PolicyEntities) bool {
			return e.Policy == policy && slices.Contains(e.Groups, actualGroupDN)
		})
		if idx < 0 {
			c.Fatalf("expected groupDN (%s) to be present in mapping list: %#v", actualGroupDN, policyResult)
		}