new BinaryPredicate<Double, Double>() {
            @Override
            public boolean apply(@Nullable Double actual, @Nullable Double expected) {
              // Test for equality to allow non-finite values to match; otherwise, use the finite
              // test.
              return actual.equals(expected)
                  || FINITE_QUANTILE_CORRESPONDENCE.compare(actual, expected);
            }
          },

  /**
   * Returns an array containing the same values as {@code array}, but guaranteed to be of a
   * specified minimum length. If {@code array} already has a length of at least {@code minLength},
   * it is returned directly. Otherwise, a new array of size {@code minLength + padding} is
   * returned, containing the values of {@code array}, and zeroes in the remaining places.
   *
   * @param array the source array

// OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
// LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
// OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
// SUCH DAMAGE.
//

package jcifs.smb1.util;

/**
 * This code is derived from the above source
 * JCIFS API

FastAPI does several things internally with Pydantic to make sure that those same rules of class inheritance are not used for the returned data filtering, otherwise you could end up returning much more data than what you expected.

This way, you can get the best of both worlds: type annotations with **tooling support** and **data filtering**.

     *         The restrictions of the returned version range will be an intersection of the restrictions
     *         of this version range and the specified version range if both version ranges have
     *         restrictions. Otherwise, the restrictions on the returned range will be empty.
     *         </p>
     *         <p>
     *         The recommended version of the returned version range will be the recommended version of

      } else if (c1 <= Character.MAX_HIGH_SURROGATE) {
        // If the high surrogate was the last character, return its inverse
        if (index == end) {
          return -c1;
        }
        // Otherwise look for the low surrogate following it
        char c2 = seq.charAt(index);
        if (Character.isLowSurrogate(c2)) {
          return Character.toCodePoint(c1, c2);
        }

      } else if (c1 <= Character.MAX_HIGH_SURROGATE) {
        // If the high surrogate was the last character, return its inverse
        if (index == end) {
          return -c1;
        }
        // Otherwise look for the low surrogate following it
        char c2 = seq.charAt(index);
        if (Character.isLowSurrogate(c2)) {
          return Character.toCodePoint(c1, c2);
        }

# libraries are language agnostic.
#
# This shared object is not used unless framework_shared_object=true (set in the
# configure script unconditionally); otherwise if it is false or undefined, the
# build is static and TensorFlow symbols (in Python only) are loaded into the
# global symbol table in order to support op registration. This means that

            this.url = url;
        }

        /**
         * Gets the URL associated with this statistics key object.
         *
         * @return the URL if set, otherwise returns the identifier
         */
        protected String getUrl() {
            if (url != null) {
                return url;
            }
            return id;
        }
    }

    /**

    }

    /**
     * Checks if duplicate term removal is enabled in configuration.
     *
     * @return true if duplicate terms should be removed, false otherwise
     */
    protected boolean isDuplicateTermRemoved() {
        final FessConfig fessConfig = ComponentUtil.getFessConfig();
        return fessConfig.isCrawlerDocumentDuplicateTermRemoved();
    }

    /**