class_docstr: Brownian tree with fixed entropy.
  
  Useful when the map from entropy -> Brownian motion shouldn't depend on the
  locations and order of the query points. (As the usual BrownianInterval
  does - note that BrownianTree is slower as a result though.)
  
  To use:
  >>> bm = BrownianTree(t0=0.0, w0=torch.zeros(4, 1))
  >>> bm(0., 0.5)
  tensor([[ 0.0733],
          [-0.5692],
          [ 0.1872],
          [-0.3889]], device='cuda:0')
code: def levy_area_approximation(self):
      return self._interval.levy_area_approximation
  
imports: import torch
  from .None import brownian_base
  from .None import brownian_interval
  from ..types import Optional, Scalar, Tensor, Tuple, Union
label_desc: Use image node_id 7 for calling the BrownianTree obj's underlying member method code with example usage: obj.levy_area_approximation() and returns: self