docstr: Returns the distribution of DeepVAR with respect to the RNN outputs.
  
  Parameters
  ----------
  rnn_outputs
      Outputs of the unrolled RNN (batch_size, seq_len, num_cells)
  scale
      Mean scale for each time series (batch_size, 1, target_dim)
  
  Returns
  -------
  distr
      Distribution instance
  distr_args
      Distribution arguments
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code: def distr(
      self,
      rnn_outputs: torch.Tensor,
      scale: torch.Tensor,
  ):
      """
      Returns the distribution of DeepVAR with respect to the RNN outputs.
  
      Parameters
      ----------
      rnn_outputs
          Outputs of the unrolled RNN (batch_size, seq_len, num_cells)
      scale
          Mean scale for each time series (batch_size, 1, target_dim)
  
      Returns
      -------
      distr
          Distribution instance
      distr_args
          Distribution arguments
      """
      distr_args = self.proj_dist_args(rnn_outputs)
  
      # compute likelihood of target given the predicted parameters
      distr = self.distr_output.distribution(distr_args, scale=scale)
      return distr, distr_args
  
imports: from turtle import distance
  from typing import List, Optional, Tuple, Union
  from matplotlib.pyplot import sca
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.torch.modules.distribution_output import DistributionOutput
  from pts.model import weighted_average
  from pts.modules import MeanScaler, NOPScaler, FeatureEmbedder
  from torch.distributions import AffineTransform, Beta, Distribution, Gamma, NegativeBinomial, Normal, Poisson, StudentT, TransformedDistribution
label_desc: Use image node_id 5 for calling the DeepVARTrainingNetwork obj's underlying member method code with example usage: obj.distr(rnn_outputs, scale) and returns: distr,  distr_args