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Results 1 - 5 of 5 for dividend (0.2 sec)
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src/main/webapp/css/bootstrap.min.css.map
!default;\n\n// Cache $rfs-base-value unit\n$rfs-base-value-unit: unit($rfs-base-value);\n\n@function divide($dividend, $divisor, $precision: 10) {\n $sign: if($dividend > 0 and $divisor > 0 or $dividend < 0 and $divisor < 0, 1, -1);\n $dividend: abs($dividend);\n $divisor: abs($divisor);\n @if $dividend == 0 {\n @return 0;\n }\n @if $divisor == 0 {\n @error \"Cannot divide by 0\";\n }\n $remainder: $dividend;\n $result: 0;\n $factor: 10;\n @while ($remainder > 0 and $precision >= 0) {\n $quotient:...
Registered: Thu Sep 04 12:52:25 UTC 2025 - Last Modified: Sun Jan 12 06:14:02 UTC 2025 - 575.5K bytes - Viewed (0) -
src/main/webapp/css/admin/bootstrap.min.css.map
$rfs-base-font-size unit\n$rfs-base-font-size-unit: unit($rfs-base-font-size);\n\n@function divide($dividend, $divisor, $precision: 10) {\n $sign: if($dividend > 0 and $divisor > 0 or $dividend < 0 and $divisor < 0, 1, -1);\n $dividend: abs($dividend);\n $divisor: abs($divisor);\n @if $dividend == 0 {\n @return 0;\n }\n @if $divisor == 0 {\n @error \"Cannot divide by 0\";\n }\n $remainder: $dividend;\n $result: 0;\n $factor: 10;\n @while ($remainder > 0 and $precision >= 0) {\n $quotient:...
Registered: Thu Sep 04 12:52:25 UTC 2025 - Last Modified: Sat Oct 26 01:49:09 UTC 2024 - 639.3K bytes - Viewed (1) -
lib/fips140/v1.0.0.zip
to compute (y * q) / 2ᵈ, rounded to nearest integer, with 1/2 // rounding up (see FIPS 203, Section 2.3). dividend := uint32(y) * q quotient := dividend >> d // (y * q) / 2ᵈ // The d'th least-significant bit of the dividend (the most significant bit // of the remainder) is 1 for the top half of the values that divide to the // same quotient, which are the ones that round up. quotient += dividend >> (d - 1) & 1 // quotient is at most (2¹¹-1) * q / 2¹¹ + 1 = 3328, so it didn't overflow. return fieldElement(quotient)...
Registered: Tue Sep 09 11:13:09 UTC 2025 - Last Modified: Wed Jan 29 15:10:35 UTC 2025 - 635K bytes - Viewed (0) -
RELEASE.md
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`.
Registered: Tue Sep 09 12:39:10 UTC 2025 - Last Modified: Mon Aug 18 20:54:38 UTC 2025 - 740K bytes - Viewed (2) -
okhttp-idna-mapping-table/src/main/resources/okhttp3/internal/idna/IdnaMappingTable.txt
Registered: Fri Sep 05 11:42:10 UTC 2025 - Last Modified: Sat Feb 10 11:25:47 UTC 2024 - 854.1K bytes - Viewed (0)