code: def _reshape_base_samples_non_interleaved(
      mvn: MultitaskMultivariateNormal,
      base_samples: Tensor,
      sample_shape: torch.Size,
  ) -> Tensor:
      r"""Reshape base samples to account for non-interleaved MT-MVNs.
  
      This method is important for making sure that the `n`th base sample
      only effects the posterior sample for the `p`th point if `p >= n`.
      Without this reshaping, for M>=2, the posterior samples for all `n`
      points would be affected.
  
      Args:
          mvn: A MultitaskMultivariateNormal distribution.
          base_samples: A `sample_shape x `batch_shape` x n x m`-dim
              tensor of base_samples.
          sample_shape: The sample shape.
  
      Returns:
          A `sample_shape x `batch_shape` x n x m`-dim tensor of
              base_samples suitable for a non-interleaved-multi-task
              or single-task covariance matrix.
      """
      if not mvn._interleaved:
          new_shape = (
              sample_shape
              + mvn._output_shape[:-2]
              + mvn._output_shape[:-3:-1]
          )
          base_samples = (
              base_samples.transpose(-1, -2)
              .view(new_shape)
              .reshape(sample_shape + mvn.loc.shape)
              .view(base_samples.shape)
          )
      return base_samples
  
imports: from __future__ import annotations
  import torch
  from gpytorch.distributions.multitask_multivariate_normal import MultitaskMultivariateNormal
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: _reshape_base_samples_non_interleaved(mvn, base_samples, sample_shape) and returns: base_samples