class_docstr: The DaskInterface allows a Dataset objects to wrap a dask
  DataFrame object. Using dask allows loading data lazily
  and performing out-of-core operations on the data, making
  it possible to work on datasets larger than memory.
  
  The DaskInterface covers almost the complete API exposed
  by the PandasInterface with two notable exceptions:
  
  1) Sorting is not supported and any attempt at sorting will
     be ignored with an warning.
  2) Dask does not easily support adding a new column to an existing
     dataframe unless it is a scalar, add_dimension will therefore
     error when supplied a non-scalar value.
  4) Not all functions can be easily applied to a dask dataframe so
     some functions applied with aggregate and reduce will not work.
code: def add_dimension(cls, dataset, dimension, dim_pos, values, vdim):
      data = dataset.data
      if dimension.name not in data.columns:
          if not np.isscalar(values):
              if len(values):
                  err = (
                      "Dask dataframe does not support assigning "
                      "non-scalar value."
                  )
                  raise NotImplementedError(err)
              values = None
          data = data.assign(**{dimension.name: values})
      return data
  
imports: import sys
  import numpy
  import pandas
  from ..None import util
  from ..dimension import Dimension
  from ..element import Element
  from ..ndmapping import NdMapping, item_check, sorted_context
  from .interface import Interface
  from .pandas import PandasInterface
label_desc: Use image node_id 16 for calling the DaskInterface obj's underlying member method code with example usage: obj.add_dimension(cls, dataset, dimension, dim_pos, values, vdim) and returns: data