code: def get_groups_and_orders(args, grouper):
      """
      `orders` is the user-supplied ordering with the remaining data-frame-supplied
      ordering appended if the column is used for grouping. It includes anything the user
      gave, for any variable, including values not present in the dataset. It's a dict
      where the keys are e.g. "x" or "color"
  
      `groups` is the dicts of groups, ordered by the order above. Its keys are
      tuples like [("value1", ""), ("value2", "")] where each tuple contains the name
      of a single dimension-group
      """
      orders = (
          {}
          if "category_orders" not in args
          else args["category_orders"].copy()
      )
  
      # figure out orders and what the single group name would be if there were one
      single_group_name = []
      unique_cache = dict()
      for col in grouper:
          if col == one_group:
              single_group_name.append("")
          else:
              if col not in unique_cache:
                  unique_cache[col] = list(
                      args["data_frame"][col].unique()
                  )
              uniques = unique_cache[col]
              if len(uniques) == 1:
                  single_group_name.append(uniques[0])
              if col not in orders:
                  orders[col] = uniques
              else:
                  orders[col] = list(
                      OrderedDict.fromkeys(list(orders[col]) + uniques)
                  )
      df = args["data_frame"]
      if len(single_group_name) == len(grouper):
          # we have a single group, so we can skip all group-by operations!
          groups = {tuple(single_group_name): df}
      else:
          required_grouper = [g for g in grouper if g != one_group]
          grouped = df.groupby(
              required_grouper, sort=False
          )  # skip one_group groupers
          group_indices = grouped.indices
          sorted_group_names = [
              g if len(required_grouper) != 1 else (g,)
              for g in group_indices
          ]
  
          for i, col in reversed(list(enumerate(required_grouper))):
              sorted_group_names = sorted(
                  sorted_group_names,
                  key=lambda g: orders[col].index(g[i])
                  if g[i] in orders[col]
                  else -1,
              )
  
          # calculate the full group_names by inserting "" in the tuple index for one_group groups
          full_sorted_group_names = [
              list(t) for t in sorted_group_names
          ]
          for i, col in enumerate(grouper):
              if col == one_group:
                  for g in full_sorted_group_names:
                      g.insert(i, "")
          full_sorted_group_names = [
              tuple(g) for g in full_sorted_group_names
          ]
  
          groups = {
              sf: grouped.get_group(s if len(s) > 1 else s[0])
              for sf, s in zip(
                  full_sorted_group_names, sorted_group_names
              )
          }
      return groups, orders
  
imports: import plotly.graph_objs
  import plotly.io
  from collections import namedtuple, OrderedDict
  from ._special_inputs import IdentityMap, Constant, Range
  from .trendline_functions import ols, lowess, rolling, expanding, ewm
  from _plotly_utils.basevalidators import ColorscaleValidator
  from plotly.colors import qualitative, sequential
  import math
  from packaging import version
  import pandas
  import numpy
  from plotly._subplots import make_subplots, _set_trace_grid_reference, _subplot_type_for_trace_type
label_desc: Use image node_id 36 for calling a global function with example usage: get_groups_and_orders(args, grouper) and returns: groups,  orders