nodes: 0, module; 1, function_definition; 2, function_name:_reset; 3, parameters; 4, block; 5, identifier:self; 6, identifier:load; 7, expression_statement; 8, expression_statement; 9, expression_statement; 10, expression_statement; 11, expression_statement; 12, expression_statement; 13, expression_statement; 14, comment:"""
          Reset sorted list load.
  
          The *load* specifies the load-factor of the list. The default load
          factor of '1000' works well for lists from tens to tens of millions of
          elements.  Good practice is to use a value that is the cube root of the
          list size.  With billions of elements, the best load factor depends on
          your usage.  It's best to leave the load factor at the default until
          you start benchmarking.
          """; 15, assignment; 16, call; 17, assignment; 18, assignment; 19, assignment; 20, call; 21, identifier:values; 22, call; 23, attribute; 24, argument_list; 25, attribute; 26, identifier:load; 27, attribute; 28, binary_operator:load >> 1; 29, attribute; 30, binary_operator:load << 1; 31, attribute; 32, argument_list; 33, identifier:reduce; 34, argument_list; 35, identifier:self; 36, identifier:_clear; 37, identifier:self; 38, identifier:_load; 39, identifier:self; 40, identifier:_half; 41, identifier:load; 42, integer:1; 43, identifier:self; 44, identifier:_dual; 45, identifier:load; 46, integer:1; 47, identifier:self; 48, identifier:_update; 49, identifier:values; 50, identifier:iadd; 51, attribute; 52, list; 53, identifier:self; 54, identifier:_lists
code: def _reset(self, load):
          """
          Reset sorted list load.
  
          The *load* specifies the load-factor of the list. The default load
          factor of '1000' works well for lists from tens to tens of millions of
          elements.  Good practice is to use a value that is the cube root of the
          list size.  With billions of elements, the best load factor depends on
          your usage.  It's best to leave the load factor at the default until
          you start benchmarking.
          """
          values = reduce(iadd, self._lists, [])
          self._clear()
          self._load = load
          self._half = load >> 1
          self._dual = load << 1
          self._update(values)