code: def _base_distribution(self, distr_args):
      if self.dim == 1:
          return self.distribution_class(*distr_args)
      else:
          return Independent(self.distribution_class(*distr_args), 1)
  
imports: from typing import Callable, Dict, Optional, Tuple
  import torch
  from torch import nn
  from torch.distributions import AffineTransform, Distribution, Independent, NegativeBinomial, Normal, StudentT, TransformedDistribution
label_desc: Use image node_id 2 for calling the DistributionOutput obj's underlying member method code with example usage: obj._base_distribution(distr_args) and returns: self, Independent