class_docstr: StatisticsElement provides a baseclass for Element types that
  compute statistics based on the input data, usually a density.
  The value dimension of such elements are therefore usually virtual
  and not computed until the element is plotted.
class_docstr_tok: ["StatisticsElement","provides","a","baseclass","for","Element","types","that","compute","statistics","based","on","the","input","data",",","usually","a","density",".","The","value","dimension","of","such","elements","are","therefore","usually","virtual","and","not","computed","until","the","element","is","plotted","."]
docstr: The Dataset that this object was created from
docstr_tok: ["The","Dataset","that","this","object","was","created","from"]
code: def dataset(self):
      """
      The Dataset that this object was created from
      """
      from . import Dataset
  
      if self._dataset is None:
          datatype = list(
              unique_iterator(self.datatype + Dataset.datatype)
          )
          dataset = Dataset(
              self,
              dataset=None,
              pipeline=None,
              transforms=None,
              vdims=[],
              datatype=datatype,
          )
          return dataset
      elif not isinstance(self._dataset, Dataset):
          return Dataset(self, _validate_vdims=False, **self._dataset)
      return self._dataset
  
imports: import numpy
  import param
  from ..core.data import Dataset
  from ..core.dimension import Dimension, process_dimensions
  from ..core.element import Element, Element2D
  from ..core.util import get_param_values, unique_iterator
  from .selection import Selection1DExpr, Selection2DExpr
label_desc: Use image node_id 2 for calling the StatisticsElement obj's underlying member method code with example usage: obj.dataset() and returns: self, dataset, Dataset