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 __call__(self, t, tb=None, return_U=False, return_A=False):
      # Deliberately called t rather than ta, for backward compatibility
      out = self._interval(t, tb, return_U=return_U, return_A=return_A)
      if tb is None and not return_U and not return_A:
          out = out + self._w0
      return out
  
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 2 for calling the BrownianTree obj's underlying member method code with example usage: obj.__call__(t, tb, return_U, return_A) and returns: out