*   Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.sample_from_datasets()` operation, which...

    /**
     * Formats code content with syntax highlighting and line numbers.
     *
     * @param prefix the line number prefix pattern
     * @param style the CSS class name for styling
     * @param mimetype the MIME type of the content (currently unused)
     * @param input the code content to format
     * @return HTML formatted code with line numbers and styling
     */

# limitations under the License.
# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

# This generates a pure python wheel of the format "*-py3-none-any.whl"

	autotune         bool
	storageClass     string
	bucketName       string
	enableSha256     bool
	enableMultipart  bool
	creds            auth.Credentials
}

// Get the max throughput and iops numbers.
func objectSpeedTest(ctx context.Context, opts speedTestOpts) chan madmin.SpeedTestResult {
	ch := make(chan madmin.SpeedTestResult, 1)
	go func() {
		defer xioutil.SafeClose(ch)

		concurrency := opts.concurrencyStart

     * The method evaluates the boost expression and converts the result to a float value.
     * Supports Integer, Long, Float, Double, and String representations of numbers.
     *
     * @param map the document data as a map of field names to values
     * @return the calculated boost value as a float, or 0.0f if evaluation fails
     */

import com.google.common.annotations.J2ktIncompatible;
import com.google.errorprone.annotations.concurrent.LazyInit;
import org.jspecify.annotations.Nullable;

/**
 * The representation of a linear transformation between real numbers {@code x} and {@code y}.
 * Graphically, this is the specification of a straight line on a plane. The transformation can be
 * expressed as {@code y = m * x + c} for finite {@code m} and {@code c}, unless it is a vertical

	if err != nil || numEntries < 0 || int(2*numEntries) < int(numEntries) {
		return nil, ErrHeader
	}

	// There should be two numbers in sparseMap for each entry.
	sparseMap := strings.Split(paxHdrs[paxGNUSparseMap], ",")
	if len(sparseMap) == 1 && sparseMap[0] == "" {
		sparseMap = sparseMap[:0]
	}
	if int64(len(sparseMap)) != 2*numEntries {

- All aggregation, conditional, type-conversion and string functions are supported.
- JSON path expressions such as `FROM S3Object[*].path` are not yet evaluated.
- Large numbers (outside of the signed 64-bit range) are not yet supported.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

        .addEqualityGroup(isNull)
        .addEqualityGroup(Equivalence.identity().equivalentTo("1"))
        .testEquals();
  }

  /*
   * We use large numbers to avoid the integer cache. Normally, we'd accomplish that merely by using
   * `new Integer` (as we do) instead of `Integer.valueOf`. However, under J2KT, `new Integer`