code: def align_partitions(*dfs):
      """Mutually partition and align DataFrame blocks
  
      This serves as precursor to multi-dataframe operations like join, concat,
      or merge.
  
      Parameters
      ----------
      dfs: sequence of dd.DataFrame, dd.Series and dd.base.Scalar
          Sequence of dataframes to be aligned on their index
  
      Returns
      -------
      dfs: sequence of dd.DataFrame, dd.Series and dd.base.Scalar
          These must have consistent divisions with each other
      divisions: tuple
          Full divisions sequence of the entire result
      result: list
          A list of lists of keys that show which data exist on which
          divisions
      """
      _is_broadcastable = partial(is_broadcastable, dfs)
      dfs1 = [
          df
          for df in dfs
          if isinstance(df, _Frame) and not _is_broadcastable(df)
      ]
      if len(dfs) == 0:
          raise ValueError("dfs contains no DataFrame and Series")
      if not all(df.known_divisions for df in dfs1):
          raise ValueError(
              "Not all divisions are known, can't align "
              "partitions. Please use `set_index` "
              "to set the index."
          )
  
      divisions = list(
          unique(merge_sorted(*[df.divisions for df in dfs1]))
      )
      if len(divisions) == 1:  # single value for index
          divisions = (divisions[0], divisions[0])
      dfs2 = [
          df.repartition(divisions, force=True)
          if isinstance(df, _Frame)
          else df
          for df in dfs
      ]
  
      result = list()
      inds = [0 for df in dfs]
      for d in divisions[:-1]:
          L = list()
          for i, df in enumerate(dfs2):
              if isinstance(df, _Frame):
                  j = inds[i]
                  divs = df.divisions
                  if j < len(divs) - 1 and divs[j] == d:
                      L.append((df._name, inds[i]))
                      inds[i] += 1
                  else:
                      L.append(None)
              else:  # Scalar has no divisions
                  L.append(None)
          result.append(L)
      return dfs2, tuple(divisions), result
  
imports: from __future__ import annotations
  import math
  import pickle
  import warnings
  from functools import partial, wraps
  import numpy
  import pandas
  from pandas.api.types import is_dtype_equal
  from tlz import merge_sorted, unique
  from dask.base import is_dask_collection, tokenize
  from dask.dataframe import methods
  from dask.dataframe.core import DataFrame, Index, Series, _concat, _deprecated_kwarg, _Frame, _maybe_from_pandas, is_broadcastable, map_partitions, new_dd_object, prefix_reduction, suffix_reduction
  from dask.dataframe.dispatch import group_split_dispatch, hash_object_dispatch
  from dask.dataframe.io import from_pandas
  from dask.dataframe.shuffle import partitioning_index, rearrange_by_divisions, shuffle, shuffle_group
  from dask.dataframe.utils import asciitable, check_meta, is_dataframe_like, is_series_like, make_meta, strip_unknown_categories
  from dask.highlevelgraph import HighLevelGraph
  from dask.layers import BroadcastJoinLayer
  from dask.utils import M, apply, get_default_shuffle_method
label_desc: Use image node_id 1 for calling a global function with example usage: align_partitions() and returns: dfs2,  tuple,  result