if count := DB.Unscoped().Model(&user).Association(key).Count(); count != value {
			t.Errorf("user's %v expects: %v, got %v", key, value, count)
		}
	}

	for key, value := range map[string]int64{"Account": 0, "Pets": 0, "Toys": 0, "Company": 1, "Manager": 1, "Team": 0, "Languages": 0, "Friends": 0} {
		if count := DB.Model(&user).Association(key).Count(); count != value {

void CompareNumericalAndManualGradients(
    Model model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, int input_index,
    float* expected_grad, int num_grad, bool use_function,
    double abs_error = 1e-2) {
  absl::Status s;
  AbstractTensorHandlePtr numerical_grad;
  {
    AbstractTensorHandle* numerical_grad_raw;
    s = CalcNumericalGrad(ctx, model, inputs, input_index, use_function,

    //
    // p4 inherits from p3
    // p3 inherits from p2
    // p2 inherits from p1
    // p1 inherits from p0
    // p0 inherits from super model
    //
    // or we can show it graphically as:
    //
    // p4 ---> p3 ---> p2 ---> p1 ---> p0 --> super model
    //
    // ----------------------------------------------------------------------

    @Test
    void testProjectInheritance() throws Exception {

 * under the License.
 */
package org.apache.maven.profiles.activation;

import java.util.Properties;

import org.apache.maven.model.Activation;
import org.apache.maven.model.ActivationProperty;
import org.apache.maven.model.Profile;
import org.codehaus.plexus.context.Context;
import org.codehaus.plexus.context.ContextException;

 * limitations under the License.
 */

package gradlebuild.docs.dsl.docbook;

import gradlebuild.docs.dsl.docbook.model.ClassDoc;
import gradlebuild.docs.dsl.docbook.model.ClassExtensionDoc;
import gradlebuild.docs.dsl.docbook.model.MethodDoc;
import org.w3c.dom.Document;
import org.w3c.dom.Element;

import java.util.Collection;

class MethodsRenderer implements ClassDocMemberRenderer {

 * limitations under the License.
 */

package gradlebuild.docs.dsl.docbook;

import gradlebuild.docs.dsl.docbook.model.ClassDoc;
import gradlebuild.docs.dsl.docbook.model.ClassExtensionDoc;
import gradlebuild.docs.dsl.docbook.model.PropertyDoc;
import org.w3c.dom.Document;
import org.w3c.dom.Element;

import java.util.Collection;

class PropertiesRenderer implements ClassDocMemberRenderer {

					"language-groovy",
					"language-java",
					"language-jvm",
					"language-native",
					"launcher",
					"logging",
					"maven",
					"messaging",
					"model-core",
					"model-groovy",
					"persistent-cache",
					"platform-base",
					"platform-jvm",
					"platform-native",
					"plugin-development",
					"plugin-use",
					"plugins-application",

import javax.annotation.processing.SupportedAnnotationTypes;
import javax.annotation.processing.SupportedSourceVersion;
import javax.lang.model.SourceVersion;
import javax.lang.model.element.Element;
import javax.lang.model.element.PackageElement;
import javax.lang.model.element.TypeElement;
import javax.tools.Diagnostic;
import javax.tools.FileObject;
import javax.tools.StandardLocation;

import java.io.BufferedReader;

 * limitations under the License.
 */

package gradlebuild.docs.dsl.docbook;

import gradlebuild.docs.dsl.docbook.model.ClassDoc;
import gradlebuild.docs.dsl.docbook.model.ExtraAttributeDoc;
import gradlebuild.docs.dsl.docbook.model.PropertyDoc;
import gradlebuild.docs.dsl.source.model.PropertyMetaData;
import org.w3c.dom.Element;
import org.w3c.dom.Node;
import org.w3c.dom.Text;
import org.gradle.api.logging.Logger;

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will