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 __repr__(self):
      if self._dt is None:
          dt = None
      else:
          dt = f"{self._dt:.3f}"
      return (
          f"{self.__class__.__name__}("
          f"t0={self._start:.3f}, "
          f"t1={self._end:.3f}, "
          f"size={self._size}, "
          f"dtype={self._dtype}, "
          f"device={repr(self._device)}, "
          f"entropy={self._entropy}, "
          f"dt={dt}, "
          f"tol={self._tol}, "
          f"pool_size={self._pool_size}, "
          f"cache_size={self._cache_size}, "
          f"levy_area_approximation={repr(self._levy_area_approximation)}"
          f")"
      )
  
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 7 for calling the BrownianInterval obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str, self, str, self, str, repr, str, self, str, dt, str, self, str, self, str, self, str, repr, str