returns q. The points may overlap. func (q *P256Point) Add(r1, r2 *P256Point) *P256Point { var sum, double P256Point r1IsInfinity := r1.isInfinity() r2IsInfinity := r2.isInfinity() pointsEqual := p256PointAddAsm(&sum, r1, r2) p256PointDoubleAsm(&double, r1) p256MovCond(&sum, &double, &sum, pointsEqual) p256MovCond(&sum, r1, &sum, r2IsInfinity) p256MovCond(&sum, r2, &sum, r1IsInfinity) return q.Set(&sum) } // Double sets q = p + p, and returns q. The points may overlap. func (q *P256Point) Double(p...

   *
   * <p>If the mapped elements contain duplicates (according to {@code comparator}), the first
   * occurrence in encounter order appears in the resulting multiset, with count equal to the sum of
   * the outputs of {@code countFunction.applyAsInt(t)} for each {@code t} mapped to that element.
   *
   * @since 33.2.0 (available since 22.0 in guava-jre)
   */

	// filename characters and needs to have bounded length.
	{
		h := sha256.New()
		h.Write([]byte("openid:" + subFromToken + ":" + issFromToken))
		bs := h.Sum(nil)
		cred.ParentUser = base64.RawURLEncoding.EncodeToString(bs)
	}

	// Deny this assume role request if the policy that the user intends to bind
	// has a sts:DurationSeconds condition, which is not satisfied as well

        Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.

            assertEquals(initial + append, content, "Content should include both initial and appended");
        }

        assertEquals(initial.length() + append.length(), file.length(), "File size should be sum of both contents");
    }

    @Test
    void testEmptyDirectory() throws Exception {
        // Test operations on empty directories
        long timestamp = System.currentTimeMillis();

			wantResult: `{"id":1,"title":"Second Record","desc":"another text","synonyms":["some","synonym","value"]}`,
		},
		{
			name:       "in-expression-with-aggregation",
			query:      "select SUM(s.id) from s3object s Where 2 in s.numbers[*] or 'some' in s.synonyms[*]",
			wantResult: `{"_1":3}`,
		},
		{
			name:  "bignum-1",
			query: `SELECT id from s3object s WHERE s.id <= 9223372036854775807`,

    val afterRemoveFileContents = if (windows) "a1234" else null

    set("a", "a1234", "a1234")
    val a = cache.edit("a")!!
    a.setString(0, "a123")
    cache.maxSize = 8 // Smaller than the sum of active edits!
    cache.flush() // Force trimToSize().
    assertThat(cache.size()).isEqualTo(expectedByteCount)
    assertThat(readFileOrNull(getCleanFile("a", 0))).isEqualTo(afterRemoveFileContents)

		if err != nil {
			writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
			return
		}

		// Set the correct hex md5sum for the fan-out stream.
		fanOutOpts.MD5Hex = hex.EncodeToString(md5w.Sum(nil))

		concurrentSize := min(runtime.GOMAXPROCS(0), 100)

		fanOutResp := make([]minio.PutObjectFanOutResponse, 0, len(fanOutEntries))
		eventArgsList := make([]eventArgs, 0, len(fanOutEntries))
		for {

	hm := hmac.New(sha1.New, []byte(secretAccessKey))
	hm.Write([]byte(stringToSign))

	// Calculate signature.
	signature := base64.StdEncoding.EncodeToString(hm.Sum(nil))

	query := req.URL.Query()
	// Handle specially for Google Cloud Storage.
	query.Set("AWSAccessKeyId", accessKeyID)
	// Fill in Expires for presigned query.

   The `read_vs_write_current_requests` metric histogram is adjusted, in the case of using API Priority and Fairness instead of max-in-flight, to divide by the relevant limit: sum of queue capacities for waiting requests, sum of seat limits for executing requests. ([#110164](https://github.com/kubernetes/kubernetes/pull/110164), [@MikeSpreitzer](https://github.com/MikeSpreitzer))