code: def forward(
      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,
      past_control: torch.Tensor,
      future_control: torch.Tensor,
  ) -> torch.Tensor:
      control_distr, distr = self.distribution(
          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,
          future_observed_values=future_observed_values,
          past_control=past_control,
          future_control=future_control,
      )
  
      # put together target sequence
      # (batch_size, seq_len, *target_shape)
      target = torch.cat(
          (
              past_target[
                  :, self.history_length - self.context_length :, ...
              ],
              future_target,
          ),
          dim=1,
      )
  
      control = torch.cat(
          (
              past_control[
                  :, self.history_length - self.context_length :, ...
              ],
              future_control,
          ),
          dim=1,
      )
  
      # (batch_size, seq_len)
      loss = -control_distr.log_prob(control) - distr.log_prob(target)
  
      # (batch_size, seq_len, *target_shape)
      observed_values = torch.cat(
          (
              past_observed_values[
                  :, self.history_length - self.context_length :, ...
              ],
              future_observed_values,
          ),
          dim=1,
      )
  
      # mask the loss at one time step iff one or more observations is missing in the target dimensions
      # (batch_size, seq_len)
      loss_weights = (
          observed_values
          if (len(self.target_shape) == 0)
          else observed_values.min(dim=-1, keepdim=False)
      )
  
      weighted_loss = weighted_average(loss, weights=loss_weights)
  
      return weighted_loss, loss
  
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 2 for calling the CausalDeepARTrainingNetwork obj's underlying member method code with example usage: obj.forward(feat_static_cat, feat_static_real, past_time_feat, past_target, past_observed_values, future_time_feat, future_target, future_observed_values, past_control, future_control) and returns: weighted_loss,  loss