if stmt.Schema != nil && len(values.Columns) >= 1 {
				selectColumns, restricted := stmt.SelectAndOmitColumns(true, true)

				columns := make([]string, 0, len(values.Columns)-1)
				for _, column := range values.Columns {
					if field := stmt.Schema.LookUpField(column.Name); field != nil {
						if v, ok := selectColumns[field.DBName]; (ok && v) || (!ok && !restricted) {

				matchedFieldCount := make(map[string]int, len(columns))
				for idx, column := range columns {
					if field := sch.LookUpField(column); field != nil && field.Readable {
						fields[idx] = field
						if count, ok := matchedFieldCount[column]; ok {
							// handle duplicate fields
							for _, selectField := range sch.Fields {
								if selectField.DBName == column && selectField.Readable {
									if count == 0 {

// Omit specify fields that you want to ignore when creating, updating and querying
func (db *DB) Omit(columns ...string) (tx *DB) {
	tx = db.getInstance()

	if len(columns) == 1 && strings.ContainsRune(columns[0], ',') {
		tx.Statement.Omits = strings.FieldsFunc(columns[0], utils.IsValidDBNameChar)
	} else {
		tx.Statement.Omits = columns
	}
	return
}

// Where add conditions
//

   * generated by applying the specified functions. If multiple inputs are mapped to the same row
   * and column pair, they will be combined with the specified merging function in encounter order.
   *
   * <p>The returned {@code Collector} will throw a {@code NullPointerException} at collection time
   * if the row, column, value, or merging functions return null on any input.
   *

	if !rows.Next() {
		t.Errorf("Should have returned at least one row")
	} else {
		columns, _ := rows.Columns()
		AssertEqual(t, columns, []string{"fake"})
	}

	rows.Close()
}

func TestSelectWithArrayInput(t *testing.T) {
	DB.Save(&User{Name: "select_with_array", Age: 42})

	var user User

The `__tablename__` attribute tells SQLAlchemy the name of the table to use in the database for each of these models.

### Create model attributes/columns

Now create all the model (class) attributes.

Each of these attributes represents a column in its corresponding database table.

We use `Column` from SQLAlchemy as the default value.

				newName = field.DBName
			}
		}

		return m.DB.Exec(
			"ALTER TABLE ? RENAME COLUMN ? TO ?",
			m.CurrentTable(stmt), clause.Column{Name: oldName}, clause.Column{Name: newName},
		).Error
	})
}

// MigrateColumn migrate column
func (m Migrator) MigrateColumn(value interface{}, field *schema.Field, columnType gorm.ColumnType) error {
	if field.IgnoreMigration {

        the model, please use `tf.compat.v1.Estimator`.
    *   Feature Columns have been upgraded to be more Eager-friendly and to work
        with Keras. As a result, `tf.feature_column.input_layer` has been
        deprecated in favor of `tf.keras.layers.DenseFeatures`. v1 feature
        columns have direct analogues in v2 except for
        `shared_embedding_columns`, which are not cross-compatible with v1 and

			} else if ref.PrimaryValue != "" {
				conds = append(conds, clause.Eq{
					Column: clause.Column{Table: rel.JoinTable.Table, Name: ref.ForeignKey.DBName},
					Value:  ref.PrimaryValue,
				})
			} else {
				conds = append(conds, clause.Eq{
					Column: clause.Column{Table: rel.JoinTable.Table, Name: ref.ForeignKey.DBName},
					Value:  clause.Column{Table: rel.FieldSchema.Table, Name: ref.PrimaryKey.DBName},
				})
			}
		}

			}},
			"SELECT * FROM `users` WHERE `users`.`id` = ? OR `name` <> ? AND `age` > ?",
			[]interface{}{"1", "jinzhu", 18},
		},
		{
			[]clause.Interface{clause.Select{}, clause.From{}, clause.Where{