def map(self, f_list: List[Callable[[np.ndarray], int]], *, axis: int = <NUM_LIT:0>, chunksize: int = <NUM_LIT:1000>, selection: np.ndarray = None) -> List[np.ndarray]:
return self.layers["<STR_LIT>"].map(f_list, axis, chunksize, selection)<EOL>
Apply a function along an axis without loading the entire dataset in memory.

Args:
        f:		Function(s) that takes a numpy ndarray as argument

        axis:		Axis along which to apply the function (0 = rows, 1 = columns)

        chunksize: Number of rows (columns) to load per chunk

        selection: Columns (rows) to include

Returns:
        numpy.ndarray result of function application
        The result is a list of numpy arrays, one per supplied function in f_list.
        This is more efficient than repeatedly calling map() one function at a time.