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]])
docstr: Initialize Brownian path.
  Arguments:
      t0: Initial time.
      w0: Initial state.
      window_size: Unused; deprecated.
docstr_tok: ["Initialize","Brownian","path",".","Arguments",":","t0",":","Initial","time",".","w0",":","Initial","state",".","window_size",":","Unused",";","deprecated","."]
code: def __init__(self, t0: Scalar, w0: Tensor, window_size: int = 8):
      """Initialize Brownian path.
      Arguments:
          t0: Initial time.
          w0: Initial state.
          window_size: Unused; deprecated.
      """
      t1 = t0 + 1
      self._w0 = w0
      self._interval = brownian_interval.BrownianInterval(
          t0=t0,
          t1=t1,
          size=w0.shape,
          dtype=w0.dtype,
          device=w0.device,
          cache_size=None,
      )
      super(BrownianPath, self).__init__()
  
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 1 to create a new BrownianPath object from inherited base classes: brownian_base with example: obj = BrownianPath(t0, w0, window_size)