class_docstr: Brownian path, storing every computed value.
  
  Useful for speed, when memory isn't a concern.
  
  To use:
  >>> bm = BrownianPath(t0=0.0, w0=torch.zeros(4, 1))
  >>> bm(0., 0.5)
  tensor([[ 0.0733],
          [-0.5692],
          [ 0.1872],
          [-0.3889]])
code: def device(self):
      return self._interval.device
  
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 5 for calling the BrownianPath obj's underlying member method code with example usage: obj.device() and returns: self