class_docstr: Brownian interval with fixed entropy.
  
  Computes increments (and optionally Levy area).
  
  To use:
  >>> bm = BrownianInterval(t0=0.0, t1=1.0, size=(4, 1), device='cuda')
  >>> bm(0., 0.5)
  tensor([[ 0.0733],
          [-0.5692],
          [ 0.1872],
          [-0.3889]], device='cuda:0')
class_docstr_tok: ["Brownian","interval","with","fixed","entropy",".","Computes","increments","(","and","optionally","Levy","area",")",".","To","use",":",">",">",">","bm","=","BrownianInterval","(","t0=0.0",",","t1=1.0",",","size=","(","4",",","1",")",",","device='cuda","'",")",">",">",">","bm","(","0.",",","0.5",")","tensor","(","[","[","0.0733","]",",","[","-0.5692","]",",","[","0.1872","]",",","[","-0.3889","]","]",",","device='cuda:0","'",")"]
code: def cache_size(self):
      return self._cache_size
  
imports: import math
  import trampoline
  import warnings
  import numpy
  import torch
  from .None import brownian_base
  from ..settings import LEVY_AREA_APPROXIMATIONS
  from ..types import Scalar, Optional, Tuple, Union, Tensor
label_desc: Use image node_id 17 for calling the BrownianInterval obj's underlying member method code with example usage: obj.cache_size() and returns: self