code: def map(self, f_list: List[Callable[[np.ndarray], int]], *, axis: int = 0, chunksize: int = 1000, selection: np.ndarray = None) -> List[np.ndarray]:
  		
  		return self.layers[""].map(f_list, axis, chunksize, selection)
docs: 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.