code: def _randn(self, seed):
      # We generate random noise deterministically wrt some seed; this seed is determined by the generator.
      # This means that if we drop out of the cache, then we'll create the same random noise next time, as we still
      # have the generator.
      size = self._top._size
      return _randn(size, self._top._dtype, self._top._device, seed)
  
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 4 for calling the _Interval obj's underlying member method code with example usage: obj._randn(seed) and returns: _randn