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 range(cls, dataset, dimension):
      import dask.dataframe as dd
  
      dimension = dataset.get_dimension(dimension, strict=True)
      column = dataset.data[dimension.name]
      if column.dtype.kind == "O":
          column = np.sort(column[column.notnull()].compute())
          return (
              (column[0], column[-1]) if len(column) else (None, None)
          )
      else:
          if dimension.nodata is not None:
              column = cls.replace_value(column, dimension.nodata)
          return dd.compute(column.min(), column.max())
  
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 7 for calling the DaskInterface obj's underlying member method code with example usage: obj.range(cls, dataset, dimension) and returns: unknown, dd