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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 map_as_series(self, func, value_size=None, dtype=None, chunk_size='auto'):
 """ Efficiently apply a function to images as series data. For images data that represent image sequences, this method applies a function to each pixel's series, and then returns to the images format, using an efficient intermediate block representation. Parameters func : function Function to apply to each time series. Should take one-dimensional ndarray and return the transformed one-dimensional ndarray. value_size : int, optional, default = None Size of the one-dimensional ndarray resulting from application of func. If not supplied, will be automatically inferred for an extra computational cost. dtype : str, optional, default = None dtype of one-dimensional ndarray resulting from application of func. If not supplied it will be automatically inferred for an extra computational cost. chunk_size : str or tuple, size of image chunk used during conversion, default = 'auto' String interpreted as memory size (in kilobytes, e.g. '64'). The exception is the string 'auto'. In spark mode, 'auto' will choose a chunk size to make the resulting blocks ~100 MB in size. In local mode, 'auto' will create a single block. Tuple of ints interpreted as 'pixels per dimension'. """

        blocks = self.toblocks(chunk_size=chunk_size)

        if value_size is not None:
            dims = list(blocks.blockshape)
            dims[0] = value_size
        else:
            dims = None

        def f(block):
            return apply_along_axis(func, 0, block)

        return blocks.map(f, value_shape=dims, dtype=dtype).toimages()