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)
  time_features
      Dynamic time features (batch_size, seq_len, num_features)
  scale
      Mean scale for each time series (batch_size, 1, target_dim)
  lags_scaled
      Scaled lags used for RNN input
      (batch_size, seq_len, target_dim, num_lags)
  target_dimension_indicator
      Indices of the target dimension (batch_size, target_dim)
  seq_len
      Length of the sequences
  
  Returns
  -------
  distr
      Distribution instance
  distr_args
      Distribution arguments
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code: def distr(
      self,
      rnn_outputs: Tensor,
      time_features: Tensor,
      scale: Tensor,
      lags_scaled: Tensor,
      target_dimension_indicator: Tensor,
      seq_len: int,
  ):
      """
      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)
      time_features
          Dynamic time features (batch_size, seq_len, num_features)
      scale
          Mean scale for each time series (batch_size, 1, target_dim)
      lags_scaled
          Scaled lags used for RNN input
          (batch_size, seq_len, target_dim, num_lags)
      target_dimension_indicator
          Indices of the target dimension (batch_size, target_dim)
      seq_len
          Length of the sequences
  
      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 typing import List, Optional, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
  from gluonts.mx.distribution import Distribution, DistributionOutput
  from gluonts.mx.util import assert_shape, weighted_average
label_desc: Use image node_id 5 for calling the DeepVARNetwork obj's underlying member method code with example usage: obj.distr(rnn_outputs, time_features, scale, lags_scaled, target_dimension_indicator, seq_len) and returns: distr,  distr_args