code: def loss(
      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,
      future_only: bool = False,
      aggregate_by=torch.mean,
  ) -> torch.Tensor:
      _, scale, hidden_state, _, _ = self.unroll_lagged_rnn(
          feat_static_cat,
          feat_static_real,
          past_time_feat,
          past_target,
          past_observed_values,
          future_time_feat,
          future_target,
      )
  
      hidden_state = hidden_state[:, : self.context_length]
  
      distr = self.output_distribution(self.picnn, hidden_state, scale)
  
      context_target = past_target[:, -self.context_length + 1 :]
      target = torch.cat((context_target, future_target), dim=1)
  
      return aggregate_by(distr.loss(target), dim=-1)
  
file_path: gluonts/src/gluonts/torch/model/mqf2/module.py
imports: from typing import List, Optional
  import torch
  from gluonts.core.component import validated
  from gluonts.torch.model.deepar.module import DeepARModel
  from cpflows.flows import ActNorm
  from cpflows.icnn import PICNN
  from .distribution import MQF2DistributionOutput
  from .icnn_utils import DeepConvexNet, SequentialNet
label_desc: Use image node_id 4 for calling the MQF2MultiHorizonModel obj's underlying member method code with example usage: obj.loss(feat_static_cat, feat_static_real, past_time_feat, past_target, past_observed_values, future_time_feat, future_target, future_observed_values, future_only, aggregate_by) and returns: aggregate_by