code: def gen_covariances(
      self,
      ndim: int,
      batch_shape: Sequence[int] = (),
      as_sqrt: bool = False,
  ) -> Tensor:
      shape = tuple(batch_shape) + (ndim, ndim)
      eigvals = -torch.rand(
          shape[:-1], **self.tkwargs
      ).log()  # exponential rvs
      orthmat = torch.linalg.svd(torch.randn(shape, **self.tkwargs)).U
      sqrt_covar = orthmat * torch.sqrt(eigvals).unsqueeze(-2)
      return (
          sqrt_covar
          if as_sqrt
          else sqrt_covar @ sqrt_covar.transpose(-2, -1)
      )
  
imports: from __future__ import annotations
  from copy import deepcopy
  from itertools import count
  from typing import Any, Dict, Optional, Sequence, Tuple
  import torch
  from botorch.utils.probability.mvnxpb import MVNXPB
  from botorch.utils.probability.truncated_multivariate_normal import TruncatedMultivariateNormal
  from botorch.utils.probability.unified_skew_normal import UnifiedSkewNormal
  from botorch.utils.testing import BotorchTestCase
  from linear_operator.operators import DenseLinearOperator
  from torch import Tensor
  from torch.distributions import MultivariateNormal
  from torch.special import ndtri
label_desc: Use image node_id 3 for calling the TestUnifiedSkewNormal obj's underlying member method code with example usage: obj.gen_covariances(ndim, batch_shape, as_sqrt) and returns: unknown