class_docstr: Helper class for creating an over window. Similar to SQL, over window aggregates compute an
  aggregate for each input row over a range of its neighboring rows.
  
  Over-windows for batch tables are currently not supported.
  
  Example:
  ::
  
      >>> from pyflink.table.expressions import col, UNBOUNDED_RANGE
      >>> Over.partition_by(col("a")) \
      ...     .order_by(col("rowtime")) \
      ...     .preceding(UNBOUNDED_RANGE) \
      ...     .alias("w")
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docstr: Specifies the time attribute on which rows are ordered.
  
  For streaming tables, reference a rowtime or proctime time attribute here
  to specify the time mode.
  
  :param order_by: Field reference.
  :return: An over window with defined order.
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code: def order_by(
      cls, order_by: Expression
  ) -> "OverWindowPartitionedOrdered":
      """
      Specifies the time attribute on which rows are ordered.
  
      For streaming tables, reference a rowtime or proctime time attribute here
      to specify the time mode.
  
      :param order_by: Field reference.
      :return: An over window with defined order.
      """
      return OverWindowPartitionedOrdered(
          get_gateway().jvm.Over.orderBy(_get_java_expression(order_by))
      )
  
imports: from py4j.java_gateway import get_method
  from pyflink.java_gateway import get_gateway
  from pyflink.table import Expression
  from pyflink.table.expression import _get_java_expression
  from pyflink.table.utils import to_expression_jarray
label_desc: Use image node_id 1 for calling the Over obj's underlying member method code with example usage: obj.order_by(cls, order_by) and returns: OverWindowPartitionedOrdered