code: def __call__(self, ta, tb=None, return_U=False, return_A=False):
      # Whether or not to negate the statistics depends on the return value of the adjoint SDE. Currently, the adjoint
      # returns negated drift and diffusion, so we don't negate here.
      return self.base_brownian(
          -tb, -ta, return_U=return_U, return_A=return_A
      )
  
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 ReverseBrownian obj's underlying member method code with example usage: obj.__call__(ta, tb, return_U, return_A) and returns: self