How to use the toolz.map function in toolz

>>> blockdims_from_blockshape((10, 0), (4, 0))
    ((4, 4, 2), (0,))
    """
    if chunks is None:
        raise TypeError("Must supply chunks= keyword argument")
    if shape is None:
        raise TypeError("Must supply shape= keyword argument")
    if np.isnan(sum(shape)) or np.isnan(sum(chunks)):
        raise ValueError("Array chunk sizes are unknown. shape: %s, chunks: %s"
                         % (shape, chunks))
    if not all(map(is_integer, chunks)):
        raise ValueError("chunks can only contain integers.")
    if not all(map(is_integer, shape)):
        raise ValueError("shape can only contain integers.")
    shape = tuple(map(int, shape))
    chunks = tuple(map(int, chunks))
    return tuple(((bd,) * (d // bd) + ((d % bd,) if d % bd else ())
                 if d else (0,))
                 for d, bd in zip(shape, chunks))

dissoc = toolz.curry(toolz.dissoc)
do = toolz.curry(toolz.do)
drop = toolz.curry(toolz.drop)
excepts = toolz.curry(toolz.excepts)
filter = toolz.curry(toolz.filter)
get = toolz.curry(toolz.get)
get_in = toolz.curry(toolz.get_in)
groupby = toolz.curry(toolz.groupby)
interpose = toolz.curry(toolz.interpose)
itemfilter = toolz.curry(toolz.itemfilter)
itemmap = toolz.curry(toolz.itemmap)
iterate = toolz.curry(toolz.iterate)
join = toolz.curry(toolz.join)
keyfilter = toolz.curry(toolz.keyfilter)
keymap = toolz.curry(toolz.keymap)
map = toolz.curry(toolz.map)
mapcat = toolz.curry(toolz.mapcat)
nth = toolz.curry(toolz.nth)
partial = toolz.curry(toolz.partial)
partition = toolz.curry(toolz.partition)
partition_all = toolz.curry(toolz.partition_all)
partitionby = toolz.curry(toolz.partitionby)
peekn = toolz.curry(toolz.peekn)
pluck = toolz.curry(toolz.pluck)
random_sample = toolz.curry(toolz.random_sample)
reduce = toolz.curry(toolz.reduce)
reduceby = toolz.curry(toolz.reduceby)
remove = toolz.curry(toolz.remove)
sliding_window = toolz.curry(toolz.sliding_window)
sorted = toolz.curry(toolz.sorted)
tail = toolz.curry(toolz.tail)
take = toolz.curry(toolz.take)

>>> list(deepmap(inc, [1, 2], n=1))
    [2, 3]
    >>> list(deepmap(inc, [(1, 2), (3, 4)], n=2))
    [(2, 3), (4, 5)]

    Works on variadic args too

    >>> add = lambda x, y: x + y
    >>> list(deepmap(add, [1, 2], [10, 20], n=1))
    [11, 22]
    """
    n = kwargs.pop('n', 1)
    if n == 0:
        return func(*data)
    if n == 1:
        return map(func, *data)
    else:
        return map(compose(tuple, partial(deepmap, func, n=n-1)), *data)

ndim = len(seq[0].shape)

    if axis < 0:
        axis = ndim + axis
    if axis >= ndim:
        msg = ("Axis must be less than than number of dimensions"
               "\nData has %d dimensions, but got axis=%d")
        raise ValueError(msg % (ndim, axis))

    if n == 1:
        return seq[0]

    if (not allow_unknown_chunksizes and
        not all(i == axis or all(x.shape[i] == seq[0].shape[i] for x in seq)
                for i in range(ndim))):
        if any(map(np.isnan, seq[0].shape)):
            raise ValueError("Tried to concatenate arrays with unknown"
                             " shape %s.  To force concatenation pass"
                             " allow_unknown_chunksizes=True."
                             % str(seq[0].shape))
        raise ValueError("Shapes do not align: %s", [x.shape for x in seq])

    inds = [list(range(ndim)) for i in range(n)]
    for i, ind in enumerate(inds):
        ind[axis] = -(i + 1)

    uc_args = list(concat(zip(seq, inds)))
    _, seq = unify_chunks(*uc_args, warn=False)

    bds = [a.chunks for a in seq]

    chunks = (seq[0].chunks[:axis] + (sum([bd[axis] for bd in bds], ()), ) +

numblocks = {a.name: a.numblocks for a, ind in arginds if ind is not None}
    argindsstr = list(concat([(a if ind is None else a.name, ind) for a, ind in arginds]))
    # Finish up the name
    if not out:
        out = '%s-%s' % (token or funcname(func).strip('_'),
                         tokenize(func, out_ind, argindsstr, dtype, **kwargs))

    dsk = top(func, out, out_ind, *argindsstr, numblocks=numblocks, **kwargs)
    dsks = [a.dask for a, ind in arginds if ind is not None]

    chunks = [chunkss[i] for i in out_ind]
    if adjust_chunks:
        for i, ind in enumerate(out_ind):
            if ind in adjust_chunks:
                if callable(adjust_chunks[ind]):
                    chunks[i] = tuple(map(adjust_chunks[ind], chunks[i]))
                elif isinstance(adjust_chunks[ind], int):
                    chunks[i] = tuple(adjust_chunks[ind] for _ in chunks[i])
                elif isinstance(adjust_chunks[ind], (tuple, list)):
                    chunks[i] = tuple(adjust_chunks[ind])
                else:
                    raise NotImplementedError(
                        "adjust_chunks values must be callable, int, or tuple")
    chunks = tuple(chunks)

    return Array(sharedict.merge((out, dsk), *dsks), out, chunks, dtype=dtype)

def run(n, x, *goals):
    """ Run a logic program.  Obtain n solutions to satisfy goals.

    n     - number of desired solutions.  See ``take``
            0 for all
            None for a lazy sequence
    x     - Output variable
    goals - a sequence of goals.  All must be true

    >>> from logpy import run, var, eq
    >>> x = var()
    >>> run(1, x, eq(x, 1))
    (1,)
    """
    results = map(partial(reify, x), goaleval(lall(*goals))({}))
    return take(n, unique(results, key=multihash))

def numblocks(self):
        return tuple(map(len, self.chunks))

{'i': 'Hello', 'j': (2, 3)}
    """
    # List like [('i', 2), ('j', 1), ('i', 1), ('j', 2)]
    argpairs2 = [(a, ind) for a, ind in argpairs if ind is not None]
    L = concat([zip(inds, dims) for (x, inds), (x, dims)
                in join(first, argpairs2, first, numblocks.items())])

    g = groupby(0, L)
    g = dict((k, set([d for i, d in v])) for k, v in g.items())

    g2 = dict((k, v - set(sentinels) if len(v) > 1 else v) for k, v in g.items())

    if consolidate:
        return valmap(consolidate, g2)

    if g2 and not set(map(len, g2.values())) == set([1]):
        raise ValueError("Shapes do not align %s" % g)

    return valmap(first, g2)

def _set_op(operator, *sets, labels=None):
    new_ids = t.reduce(operator, t.map(lambda s: s.artifact_ids, sets))
    return ArtifactSet(new_ids, labels)

>>> common_blockdim([(1, 2), (2, 1)])
    (1, 1, 1)
    >>> common_blockdim([(2, 2), (3, 1)])  # doctest: +SKIP
    Traceback (most recent call last):
        ...
    ValueError: Chunks do not align
    """
    if not any(blockdims):
        return ()
    non_trivial_dims = set([d for d in blockdims if len(d) > 1])
    if len(non_trivial_dims) == 1:
        return first(non_trivial_dims)
    if len(non_trivial_dims) == 0:
        return max(blockdims, key=first)

    if np.isnan(sum(map(sum, blockdims))):
        raise ValueError("Arrays chunk sizes are unknown: %s", blockdims)

    if len(set(map(sum, non_trivial_dims))) > 1:
        raise ValueError("Chunks do not add up to same value", blockdims)

    # We have multiple non-trivial chunks on this axis
    # e.g. (5, 2) and (4, 3)

    # We create a single chunk tuple with the same total length
    # that evenly divides both, e.g. (4, 1, 2)

    # To accomplish this we walk down all chunk tuples together, finding the
    # smallest element, adding it to the output, and subtracting it from all
    # other elements and remove the element itself.  We stop once we have
    # burned through all of the chunk tuples.
    # For efficiency's sake we reverse the lists so that we can pop off the end