code: def setUp(
      self,
      nprobs_per_coeff: int = 3,
      bound_range: Tuple[float, float] = (-3.0, 3.0),
      mc_num_samples: int = 10000,
      mc_batch_limit: int = 1000,
      mc_atol_multiplier: float = 4.0,
      seed: int = 1,
      dtype: torch.dtype = torch.float64,
      device: Optional[torch.device] = None,
  ):
      super().setUp()
      self.dtype = dtype
      self.seed_generator = count(seed)
      self.nprobs_per_coeff = nprobs_per_coeff
      self.mc_num_samples = mc_num_samples
      self.mc_batch_limit = mc_batch_limit
      self.mc_atol_multiplier = mc_atol_multiplier
  
      pos_coeffs = torch.cat(
          [
              torch.linspace(0, 1, 5, **self.tkwargs),
              torch.tensor(
                  [0.01, 0.05, 0.924, 0.925, 0.99], **self.tkwargs
              ),
          ]
      )
      self.correlations = torch.cat([pos_coeffs, -pos_coeffs[1:]])
  
      with torch.random.fork_rng():
          torch.manual_seed(next(self.seed_generator))
          _lower = torch.rand(
              nprobs_per_coeff,
              len(self.correlations),
              2,
              **self.tkwargs,
          )
          _upper = _lower + (1 - _lower) * torch.rand_like(_lower)
  
      self.lower_bounds = (
          bound_range[0] + (bound_range[1] - bound_range[0]) * _lower
      )
      self.upper_bounds = (
          bound_range[0] + (bound_range[1] - bound_range[0]) * _upper
      )
  
      self.sqrt_covariances = torch.zeros(
          len(self.correlations), 2, 2, **self.tkwargs
      )
      self.sqrt_covariances[:, 0, 0] = 1
      self.sqrt_covariances[:, 1, 0] = self.correlations
      self.sqrt_covariances[:, 1, 1] = (
          1 - self.correlations**2
      ) ** 0.5
  
imports: from __future__ import annotations
  from itertools import count
  from typing import Any, Callable, Dict, Optional, Tuple, Union
  import torch
  from botorch.exceptions import UnsupportedError
  from botorch.utils.probability.bvn import _bvnu_polar, _bvnu_taylor, bvn, bvnmom, bvnu, Phi
  from botorch.utils.testing import BotorchTestCase
  from torch import Tensor
label_desc: Use image node_id 1 for calling the TestBVN obj's underlying member method code with example usage: obj.setUp(nprobs_per_coeff, bound_range, mc_num_samples, mc_batch_limit, mc_atol_multiplier, seed, dtype, device) without return types