code: def select_mask(cls, dataset, selection):
          
          select_mask = None
          for dim, k in selection.items():
              if isinstance(k, tuple):
                  k = slice(*k)
              masks = []
              alias = dataset.get_dimension(dim).name
              series = dataset.data[alias]
              if isinstance(k, slice):
                  if k.start is not None:
                      # Workaround for dask issue #3392
                      kval = util.numpy_scalar_to_python(k.start)
                      masks.append(kval <= series)
                  if k.stop is not None:
                      kval = util.numpy_scalar_to_python(k.stop)
                      masks.append(series < kval)
              elif isinstance(k, (set, list)):
                  iter_slc = None
                  for ik in k:
                      mask = series == ik
                      if iter_slc is None:
                          iter_slc = mask
                      else:
                          iter_slc |= mask
                  masks.append(iter_slc)
              elif callable(k):
                  masks.append(k(series))
              else:
                  masks.append(series == k)
              for mask in masks:
                  if select_mask is not None:
                      select_mask &= mask
                  else:
                      select_mask = mask
          return select_mask
docs: Given a Dataset object and a dictionary with dimension keys and
          selection keys (i.e tuple ranges, slices, sets, lists or literals)
          return a boolean mask over the rows in the Dataset object that
          have been selected.