human
Complete the below
@insert_meta_param_description
def apply_concat_apply(args, chunk=None, aggregate=None, combine=None, meta=no_default, token=None, chunk_kwargs=None, aggregate_kwargs=None, combine_kwargs=None, split_every=None, split_out=None, split_out_setup=None, split_out_setup_kwargs=None, **kwargs):

    ''''Apply a function to blocks, then concat, then apply again
    Parameters
    args :
    Positional arguments for the `chunk` function. All `dask.dataframe`
    objects should be partitioned and indexed equivalently.
    chunk : function [block-per-arg] -> block
    Function to operate on each block of data
    aggregate : function concatenated-block -> block
    Function to operate on the concatenated result of chunk
    combine : function concatenated-block -> block, optional
    Function to operate on intermediate concatenated results of chunk
    in a tree-reduction. If not provided, defaults to aggregate.
    $META
    token : str, optional
    The name to use for the output keys.
    chunk_kwargs : dict, optional
    Keywords for the chunk function only.
    aggregate_kwargs : dict, optional
    Keywords for the aggregate function only.
    combine_kwargs : dict, optional
    Keywords for the combine function only.
    split_every : int, optional
    Group partitions into groups of this size while performing a
    tree-reduction. If set to False, no tree-reduction will be used,
    and all intermediates will be concatenated and passed to ``aggregate``.
    Default is 8.
    split_out : int, optional
    Number of output partitions. Split occurs after first chunk reduction.
    split_out_setup : callable, optional
    If provided, this function is called on each chunk before performing
    the hash-split. It should return a pandas object, where each row
    (excluding the index) is hashed. If not provided, the chunk is hashed
    as is.
    split_out_setup_kwargs : dict, optional
    Keywords for the `split_out_setup` function only.
    kwargs :
    All remaining keywords will be passed to ``chunk``, ``aggregate``, and
    ``combine``.
    Examples
    >>> def chunk(a_block, b_block):
    ...     pass
    >>> def agg(df):
    ...     pass
    >>> apply_concat_apply([a, b], chunk=chunk, aggregate=agg)  # doctest: +SKIP'
    '''
assistant
#Here is the Python program you requested.
    if (chunk_kwargs is None):
        chunk_kwargs = dict()
    if (aggregate_kwargs is None):
        aggregate_kwargs = dict()
    chunk_kwargs.update(kwargs)
    aggregate_kwargs.update(kwargs)
    if (combine is None):
        if combine_kwargs:
            raise ValueError('`combine_kwargs`    provided    with    no    `combine`')
        combine = aggregate
        combine_kwargs = aggregate_kwargs
    else:
        if (combine_kwargs is None):
            combine_kwargs = dict()
        combine_kwargs.update(kwargs)
    if (not isinstance(args, (tuple, list))):
        args = [args]
    npartitions = set((arg.npartitions for arg in args if isinstance(arg, _Frame)))
    if (len(npartitions) > 1):
        raise ValueError('All    arguments    must    have    same    number    of    partitions')
    npartitions = npartitions.pop()
    if (split_every is None):
        split_every = 8
    elif (split_every is False):
        split_every = npartitions
    elif ((split_every < 2) or (not isinstance(split_every, int))):
        raise ValueError('split_every    must    be    an    integer    >=    2')
    token_key = tokenize((token or (chunk, aggregate)), meta, args, chunk_kwargs, aggregate_kwargs, combine_kwargs, split_every, split_out, split_out_setup, split_out_setup_kwargs)
    a = '{0}-chunk-{1}'.format((token or funcname(chunk)), token_key)
    if ((len(args) == 1) and isinstance(args[0], _Frame) and (not chunk_kwargs)):
        dsk = {(a, 0, i, 0): (chunk, key) for (i, key) in enumerate(args[0]._keys())}
    else:
        dsk = {(a, 0, i, 0): (apply, chunk, [((x._name, i) if isinstance(x, _Frame) else x) for x in args], chunk_kwargs) for i in range(args[0].npartitions)}
    if (split_out and (split_out > 1)):
        split_prefix = ('split-%s' % token_key)
        shard_prefix = ('shard-%s' % token_key)
        for i in range(args[0].npartitions):
            dsk[(split_prefix, i)] = (hash_shard, (a, 0, i, 0), split_out, split_out_setup, split_out_setup_kwargs)
            for j in range(split_out):
 ...