class_docstr: Constructs a multi-output multivariate Normal random variable, based on mean and covariance
  Can be multi-output multivariate, or a batch of multi-output multivariate Normal
  
  Passing a matrix mean corresponds to a multi-output multivariate Normal
  Passing a matrix mean corresponds to a batch of multivariate Normals
  
  :param torch.Tensor mean:  An `n x t` or batch `b x n x t` matrix of means for the MVN distribution.
  :param ~linear_operator.operators.LinearOperator covar: An `... x NT x NT` (batch) matrix.
      covariance matrix of MVN distribution.
  :param bool validate_args: (default=False) If True, validate `mean` and `covariance_matrix` arguments.
  :param bool interleaved: (default=True) If True, covariance matrix is interpreted as block-diagonal w.r.t.
      inter-task covariances for each observation. If False, it is interpreted as block-diagonal
      w.r.t. inter-observation covariance for each task.
docstr: Convert a multitask MVN into a batched (non-multitask) MVNs
  The result retains the intertask covariances, but gets rid of the inter-data covariances.
  The resulting distribution will have ``len(mvns)`` tasks, and the tasks will be independent.
  
  :returns: the bached data-independent MVN
  :rtype: gpytorch.distributions.MultivariateNormal
docstr_tok: ["Convert","a","multitask","MVN","into","a","batched","(","non-multitask",")","MVNs","The","result","retains","the","intertask","covariances",",","but","gets","rid","of","the","inter-data","covariances",".","The","resulting","distribution","will","have","``","len","(","mvns",")","``","tasks",",","and","the","tasks","will","be","independent",".",":","returns",":","the","bached","data-independent","MVN",":","rtype",":","gpytorch.distributions.MultivariateNormal"]
code: def to_data_independent_dist(self, jitter_val=1e-4):
      """
      Convert a multitask MVN into a batched (non-multitask) MVNs
      The result retains the intertask covariances, but gets rid of the inter-data covariances.
      The resulting distribution will have ``len(mvns)`` tasks, and the tasks will be independent.
  
      :returns: the bached data-independent MVN
      :rtype: gpytorch.distributions.MultivariateNormal
      """
      # Create batch distribution where all data are independent, but the tasks are dependent
      full_covar = self.lazy_covariance_matrix
      num_data, num_tasks = self.mean.shape[-2:]
      if self._interleaved:
          data_indices = torch.arange(
              0,
              num_data * num_tasks,
              num_tasks,
              device=full_covar.device,
          ).view(-1, 1, 1)
          task_indices = torch.arange(
              num_tasks, device=full_covar.device
          )
      else:
          data_indices = torch.arange(
              num_data, device=full_covar.device
          ).view(-1, 1, 1)
          task_indices = torch.arange(
              0,
              num_data * num_tasks,
              num_data,
              device=full_covar.device,
          )
      task_covars = full_covar[
          ...,
          data_indices + task_indices.unsqueeze(-2),
          data_indices + task_indices.unsqueeze(-1),
      ]
      return MultivariateNormal(
          self.mean,
          to_linear_operator(task_covars).add_jitter(
              jitter_val=jitter_val
          ),
      )
  
imports: import torch
  from linear_operator import LinearOperator, to_linear_operator
  from linear_operator.operators import BlockDiagLinearOperator, BlockInterleavedLinearOperator, CatLinearOperator, DiagLinearOperator
  from .multivariate_normal import MultivariateNormal
label_desc: Use image node_id 13 for calling the MultitaskMultivariateNormal obj's underlying member method code with example usage: obj.to_data_independent_dist(jitter_val) and returns: MultivariateNormal