code: def _maybe_align_partitions(args):
      """Align DataFrame blocks if divisions are different.
  
      Note that if all divisions are unknown, but have equal npartitions, then
      they will be passed through unchanged. This is different than
      `align_partitions`, which will fail if divisions aren't all known
      """
      _is_broadcastable = partial(is_broadcastable, args)
      dfs = [
          df
          for df in args
          if isinstance(df, _Frame) and not _is_broadcastable(df)
      ]
      if not dfs:
          return args
  
      divisions = dfs[0].divisions
      if not all(df.divisions == divisions for df in dfs):
          dfs2 = iter(align_partitions(*dfs)[0])
          return [
              a if not isinstance(a, _Frame) else next(dfs2)
              for a in args
          ]
      return args
  
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 2 for calling a global function with example usage: _maybe_align_partitions(args) and returns: args, args, unknown