code: def distribution(
      self,
      feat_static_cat: torch.Tensor,
      feat_static_real: torch.Tensor,
      past_time_feat: torch.Tensor,
      past_target: torch.Tensor,
      past_observed_values: torch.Tensor,
      future_time_feat: torch.Tensor,
      future_target: torch.Tensor,
      future_observed_values: torch.Tensor,
  ) -> Distribution:
      rnn_outputs, _, scale, _ = self.unroll_encoder(
          feat_static_cat=feat_static_cat,
          feat_static_real=feat_static_real,
          past_time_feat=past_time_feat,
          past_target=past_target,
          past_observed_values=past_observed_values,
          future_time_feat=future_time_feat,
          future_target=future_target,
      )
  
      distr_args = self.proj_distr_args(rnn_outputs)
  
      return self.distr_output.distribution(distr_args, scale=scale)
  
imports: from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from torch.distributions import Distribution
  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
label_desc: Use image node_id 1 for calling the DeepARTrainingNetwork obj's underlying member method code with example usage: obj.distribution(feat_static_cat, feat_static_real, past_time_feat, past_target, past_observed_values, future_time_feat, future_target, future_observed_values) and returns: self