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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fromlist(items, accessor=None, index=None, labels=None, dtype=None, npartitions=None, engine=None):
 """ Load series data from a list with an optional accessor function. Will call accessor function on each item from the list, providing a generic interface for data loading. Parameters items : list A list of items to load. accessor : function, optional, default = None A function to apply to each item in the list during loading. index : array, optional, default = None where N is the length of each record. labels : array, optional, default = None Labels for records. If provided, should have same length as items. dtype : string, default = None Data numerical type (if provided will avoid check) npartitions : int, default = None Number of partitions for parallelization (Spark only) engine : object, default = None Computational engine (e.g. a SparkContext for Spark) """

    if spark and isinstance(engine, spark):
        if dtype is None:
            dtype = accessor(items[0]).dtype if accessor else items[0].dtype
        nrecords = len(items)
        keys = map(lambda k: (k, ), range(len(items)))
        if not npartitions:
            npartitions = engine.defaultParallelism
        items = zip(keys, items)
        rdd = engine.parallelize(items, npartitions)
        if accessor:
            rdd = rdd.mapValues(accessor)
        return fromrdd(rdd, nrecords=nrecords, index=index, labels=labels, dtype=dtype, ordered=True)

    else:
        if accessor:
            items = [accessor(i) for i in items]
        return fromarray(items, index=index, labels=labels)