class_docstr: Interface for simple dictionary-based dataset format using a
  compressed representation that uses the cartesian product between
  key dimensions. As with DictInterface, the dictionary keys correspond
  to the column (i.e. dimension) names and the values are NumPy arrays
  representing the values in that column.
  
  To use this compressed format, the key dimensions must be orthogonal
  to one another with each key dimension specifying an axis of the
  multidimensional space occupied by the value dimension data. For
  instance, given an temperature recordings sampled regularly across
  the earth surface, a list of N unique latitudes and M unique
  longitudes can specify the position of NxM temperature samples.
code: def shape(cls, dataset, gridded=False):
      vdim_tuple = cls.packed(dataset)
      if vdim_tuple:
          shape = dataset.data[vdim_tuple].shape[:-1]
      else:
          shape = dataset.data[dataset.vdims[0].name].shape
      if gridded:
          return shape
      else:
          return (
              np.prod(shape, dtype=np.intp),
              len(dataset.dimensions()),
          )
  
imports: from collections import defaultdict
  import numpy
  from ..None import util
  from ..dimension import dimension_name
  from ..element import Element
  from ..ndmapping import NdMapping, item_check, sorted_context
  from .dictionary import DictInterface
  from .interface import DataError, Interface
  from .util import dask_array_module, finite_range, get_array_types, is_dask
label_desc: Use image node_id 10 for calling the GridInterface obj's underlying member method code with example usage: obj.shape(cls, dataset, gridded) and returns: shape, np,  len