)

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.

			if cv.Statement.SQL.Len() > 0 {
				var (
					vars = subdb.Statement.Vars
					sql  = cv.Statement.SQL.String()
				)

				subdb.Statement.Vars = make([]interface{}, 0, len(vars))
				for _, vv := range vars {
					subdb.Statement.Vars = append(subdb.Statement.Vars, vv)
					bindvar := strings.Builder{}
					cv.BindVarTo(&bindvar, subdb.Statement, vv)

	subdb.DryRun = true

	if stmt, ok := builder.(*Statement); ok {
		if subdb.Statement.SQL.Len() > 0 {
			var (
				vars = subdb.Statement.Vars
				sql  = subdb.Statement.SQL.String()
			)

			subdb.Statement.Vars = make([]interface{}, 0, len(vars))
			for _, vv := range vars {
				subdb.Statement.Vars = append(subdb.Statement.Vars, vv)
				bindvar := strings.Builder{}

	err := gorm.G[User](DB).CreateInBatches(ctx, &users, len(users))
	if err != nil {
		t.Fatalf("CreateInBatches failed: %v", err)
	}

	filterName1 := func(stmt *gorm.Statement) {
		stmt.Where("name = ?", "GenericsScopes1")
	}

	results, err := gorm.G[User](DB).Scopes(filterName1).Find(ctx)
	if err != nil {
		t.Fatalf("Scopes failed: %v", err)
	}