code: def _draw_from_base_covar(
      self, covar: Union[Tensor, LinearOperator], base_samples: Tensor
  ) -> Tensor:
      # Now reparameterize those base samples
      if not isinstance(covar, LinearOperator):
          covar = to_linear_operator(covar)
      covar_root = covar.root_decomposition().root
      # If necessary, adjust base_samples for rank of root decomposition
      if covar_root.shape[-1] < base_samples.shape[-2]:
          base_samples = base_samples[..., : covar_root.shape[-1], :]
      elif covar_root.shape[-1] > base_samples.shape[-2]:
          raise RuntimeError("Incompatible dimension of `base_samples`")
      # the mean is included in the posterior forwards so is not included here
      res = covar_root.matmul(base_samples)
  
      return res.squeeze(-1)
  
file_path: botorch/botorch/posteriors/multitask.py
imports: from typing import Optional, Tuple, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from gpytorch.distributions import MultivariateNormal
  from linear_operator.operators import LinearOperator, to_linear_operator
  from torch import Tensor
label_desc: Use image node_id 7 for calling the MultitaskGPPosterior obj's underlying member method code with example usage: obj._draw_from_base_covar(covar, base_samples) and returns: res