public void mixesInGeneratedSubclassInterface() throws Exception {
        Bean bean = newInstance(Bean.class);
        assertTrue(bean instanceof GeneratedSubclass);
        assertEquals(Bean.class, ((GeneratedSubclass) bean).publicType());
        assertEquals(Bean.class, GeneratedSubclasses.unpackType(bean));
        assertEquals(Bean.class, GeneratedSubclasses.unpack(bean.getClass()));
    }

    @Test

        }
        1 * action.execute(_) >> { Bean bean ->
            bean.value += " > initialize"
        }
        1 * action.execute(_) >> { Bean bean ->
            bean.value += " > mutate"
        }
        1 * action.execute(_) >> { Bean bean ->
            bean.value += " > finalize"
        }
        1 * action.execute(_) >> { Bean bean ->
            assert bean.value == "create > defaults > initialize > mutate > finalize"

//
// When is_training is set to true, the given variance and mean are not used.
// In above calculation, they are replaced by new values. These new mean and
// variance are calculated as following:
// new_mean = mean(x, axis=[0, 1, 2])
// new_variance = mean(squared_difference(x, new_mean), axis=[0, 1, 2])
//
// The DDR rule for the is_training equals true case is as following:

// the specified mean.
func fastexprand(mean int) int32 {
	// Avoid overflow. Maximum possible step is
	// -ln(1/(1<<randomBitCount)) * mean, approximately 20 * mean.
	switch {
	case mean > 0x7000000:
		mean = 0x7000000
	case mean == 0:
		return 0
	}

	// Take a random sample of the exponential distribution exp(-mean*x).

            }

            class SomeTask extends DefaultTask {
                private final bean = new SomeBean()

                SomeTask() {
                    bean.gradle = project.gradle
                    bean.nested.gradle = project.gradle
                    bean.nested.project = project
                }

                @TaskAction
                void run() {

 */
package org.codelibs.fess.es.config.cbean.ca.bs;

import org.codelibs.fess.es.config.allcommon.EsAbstractConditionAggregation;
import org.codelibs.fess.es.config.allcommon.EsAbstractConditionQuery;
import org.codelibs.fess.es.config.cbean.ca.BoostDocumentRuleCA;
import org.codelibs.fess.es.config.cbean.cq.BoostDocumentRuleCQ;
import org.codelibs.fess.es.config.cbean.cq.bs.BsBoostDocumentRuleCQ;

  // CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1.000000e-03> : tensor<f32>
  // CHECK:  %[[MEAN:.*]] = "tf.Mean"(%arg0, %[[CST]]) <{keep_dims = false}> : (tensor<1x1x6x2xf32>, tensor<3xi32>) -> tensor<2xf32>
  // CHECK:  %[[SQ:.*]] = "tf.SquaredDifference"(%arg0, %[[MEAN]]) : (tensor<1x1x6x2xf32>, tensor<2xf32>) -> tensor<1x1x6x2xf32>
  // CHECK:  %[[MEAN0:.*]] = "tf.Mean"(%[[SQ]], %[[CST]]) <{keep_dims = false}> : (tensor<1x1x6x2xf32>, tensor<3xi32>) -> tensor<2xf32>

 */
package org.codelibs.fess.es.config.cbean.ca.bs;

import org.codelibs.fess.es.config.allcommon.EsAbstractConditionAggregation;
import org.codelibs.fess.es.config.allcommon.EsAbstractConditionQuery;
import org.codelibs.fess.es.config.cbean.ca.DuplicateHostCA;
import org.codelibs.fess.es.config.cbean.cq.DuplicateHostCQ;
import org.codelibs.fess.es.config.cbean.cq.bs.BsDuplicateHostCQ;

 */
package org.codelibs.fess.es.config.cbean.ca.bs;

import org.codelibs.fess.es.config.allcommon.EsAbstractConditionAggregation;
import org.codelibs.fess.es.config.allcommon.EsAbstractConditionQuery;
import org.codelibs.fess.es.config.cbean.ca.RoleTypeCA;
import org.codelibs.fess.es.config.cbean.cq.RoleTypeCQ;
import org.codelibs.fess.es.config.cbean.cq.bs.BsRoleTypeCQ;

 */
package org.codelibs.fess.es.config.cbean.ca.bs;

import org.codelibs.fess.es.config.allcommon.EsAbstractConditionAggregation;
import org.codelibs.fess.es.config.allcommon.EsAbstractConditionQuery;
import org.codelibs.fess.es.config.cbean.ca.RelatedContentCA;
import org.codelibs.fess.es.config.cbean.cq.RelatedContentCQ;
import org.codelibs.fess.es.config.cbean.cq.bs.BsRelatedContentCQ;