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.
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docstr: Return the lower and upper bounds of values along dimension.
  
  Args:
      dimension: The dimension to compute the range on.
      data_range (bool): Compute range from data values
      dimension_range (bool): Include Dimension ranges
          Whether to include Dimension range and soft_range
          in range calculation
  
  Returns:
      Tuple containing the lower and upper bound
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code: def range(self, dim, data_range=True, dimension_range=True):
      """Return the lower and upper bounds of values along dimension.
  
      Args:
          dimension: The dimension to compute the range on.
          data_range (bool): Compute range from data values
          dimension_range (bool): Include Dimension ranges
              Whether to include Dimension range and soft_range
              in range calculation
  
      Returns:
          Tuple containing the lower and upper bound
      """
      iskdim = self.get_dimension(dim) not in self.vdims
      return super().range(dim, iskdim, dimension_range)
  
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 3 for calling the StatisticsElement obj's underlying member method code with example usage: obj.range(dim, data_range, dimension_range) and returns: super