## Highlights

*   TF 2.0 delivers Keras as the central high level API used to build and train
    models. Keras provides several model-building APIs such as Sequential,
    Functional, and Subclassing along with eager execution, for immediate
    iteration and intuitive debugging, and `tf.data`, for building scalable
    input pipelines. Checkout
    [guide](https://www.tensorflow.org/beta/guide/keras/overview) for additional

  }

  /** Returns the number of pairs in the dataset. */
  public long count() {
    return xStats.count();
  }

  /** Returns an immutable snapshot of the statistics on the {@code x} values alone. */
  public Stats xStats() {
    return xStats.snapshot();
  }

  /** Returns an immutable snapshot of the statistics on the {@code y} values alone. */
  public Stats yStats() {
    return yStats.snapshot();
  }

 * maximumSize}, {@link #maximumWeight(long) maximumWeight}, {@link #expireAfterWrite
 * expireAfterWrite}, {@link #expireAfterAccess expireAfterAccess}, {@link #weakKeys weakKeys},
 * {@link #weakValues weakValues}, or {@link #softValues softValues} are requested.
 *
 * <p>If {@link #maximumSize(long) maximumSize} or {@link #maximumWeight(long) maximumWeight} is

        .isEqualTo(1);
    assertThat(
            Longs.indexOf(
                new long[] {(long) 2, (long) 3, (long) 2, (long) 3, (long) 4, (long) 2, (long) 3},
                new long[] {(long) 2, (long) 3, (long) 4}))
        .isEqualTo(2);
    assertThat(
            Longs.indexOf(
                new long[] {(long) 2, (long) 2, (long) 3, (long) 4, (long) 2, (long) 3, (long) 4},

      Long[] suffix = {(long) 86, (long) 99};
      Long[] all = concat(concat(prefix, elements), suffix);
      return asList(all).subList(2, elements.length + 2);
    }
  }

  private static Long[] concat(Long[] left, Long[] right) {
    Long[] result = new Long[left.length + right.length];
    System.arraycopy(left, 0, result, 0, left.length);
    System.arraycopy(right, 0, result, left.length, right.length);

// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Affero General Public License for more details.
//
// You should have received a copy of the GNU Affero General Public License
// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package cmd

import (
	"bytes"
	"context"
	"encoding/base64"
	"encoding/binary"
	"encoding/json"
	"encoding/xml"
	"errors"
	"fmt"

            + Long.numberOfLeadingZeros(~a)
            + Long.numberOfLeadingZeros(b)
            + Long.numberOfLeadingZeros(~b);
    if (leadingZeros > Long.SIZE + 1) {
      return a * b;
    }
    // the return value if we will overflow (which we calculate by overflowing a long :) )
    long limit = Long.MAX_VALUE + ((a ^ b) >>> (Long.SIZE - 1));
    if (leadingZeros < Long.SIZE | (a < 0 & b == Long.MIN_VALUE)) {

  private static final int ARRAY_MASK = 0x0ffff;
  private static final Random RANDOM_SOURCE = new Random(314159265358979L);
  private static final long[] longs = new long[ARRAY_SIZE];
  private static final long[] divisors = new long[ARRAY_SIZE];
  private static final String[] decimalStrings = new String[ARRAY_SIZE];
  private static final String[] binaryStrings = new String[ARRAY_SIZE];

    assertMean(0, 0, 0);
    assertMean(-1, -1, -1);
    assertMean(Long.MIN_VALUE, Long.MIN_VALUE, Long.MIN_VALUE);
    assertMean(Long.MAX_VALUE, Long.MAX_VALUE, Long.MAX_VALUE);

    // Exhaustive checks
    for (long x : ALL_LONG_CANDIDATES) {
      for (long y : ALL_LONG_CANDIDATES) {
        assertMean(x, y);
      }
    }
  }

  /** Helper method that asserts the arithmetic mean of x and y is equal to the expectedMean. */

        }
    }

    public String formatProgress(long progressedSize, long size) {
        StringBuilder sb = new StringBuilder();
        formatProgress(sb, progressedSize, size);
        return sb.toString();
    }

    public void formatProgress(StringBuilder builder, long progressedSize, long size) {
        if (progressedSize < 0L) {