docstr: Aggregate target at the given aggregate level along with updating observed value and pad indicators.
  
  :param agg_level:
  :param past_target:
  :param past_observed_values:
  :param past_is_pad:
  :param future_target:
  :param future_observed_values:
  :return:
docstr_tok: ["Aggregate","target","at","the","given","aggregate","level","along","with","updating","observed","value","and","pad","indicators",".",":","param","agg_level",":",":","param","past_target",":",":","param","past_observed_values",":",":","param","past_is_pad",":",":","param","future_target",":",":","param","future_observed_values",":",":","return",":"]
code: def get_target_related_feat_at_agg_level(
      self,
      agg_level: int,
      past_target: Tensor,
      past_observed_values: Tensor,
      past_is_pad: Tensor,
      future_target: Optional[Tensor] = None,
      future_observed_values: Optional[Tensor] = None,
  ) -> Dict:
      """
      Aggregate target at the given aggregate level along with updating observed value and pad indicators.
  
      :param agg_level:
      :param past_target:
      :param past_observed_values:
      :param past_is_pad:
      :param future_target:
      :param future_observed_values:
      :return:
      """
      agg_multiple = self.temporal_hierarchy.agg_multiples[agg_level]
  
      # Truncating the history length of the base time series to the nearest multiple.
      base_history_length = (
          past_target.shape[1] // agg_multiple
      ) * agg_multiple
  
      past_target_agg = (
          utils.agg_series(
              past_target.slice_axis(
                  axis=1, begin=-base_history_length, end=None
              ),
              agg_multiple=agg_multiple,
          )
          .squeeze(axis=-1)
          .slice_axis(
              axis=1,
              begin=-self.models[agg_level].history_length,
              end=None,
          )
      )
  
      past_is_pad_agg = (
          utils.agg_series(
              past_is_pad.slice_axis(
                  axis=1, begin=-base_history_length, end=None
              ),
              agg_multiple=agg_multiple,
          )
          .squeeze(axis=-1)
          .slice_axis(
              axis=1,
              begin=-self.models[agg_level].history_length,
              end=None,
          )
      )
  
      past_is_pad_agg = mx.nd.where(
          past_is_pad_agg == 0.0,
          mx.nd.zeros_like(past_is_pad_agg),
          mx.nd.ones_like(past_is_pad_agg),
      )
  
      past_observed_values_agg = (
          utils.agg_series(
              past_observed_values.slice_axis(
                  axis=1, begin=-base_history_length, end=None
              ),
              agg_multiple=agg_multiple,
          )
          .squeeze(axis=-1)
          .slice_axis(
              axis=1,
              begin=-self.models[agg_level].history_length,
              end=None,
          )
      )
  
      past_observed_values_agg = mx.nd.where(
          # We sum observed values of base time series at `agg_multiple` time steps;
          # if all of them are 1, then the observed value for the aggregated time series is 1 and 0 otherwise.
          # We could redefine agg_series to actually compute mean, but overloading that term might cause other
          # problems later.
          past_observed_values_agg == agg_multiple,
          mx.nd.ones_like(past_observed_values_agg),
          mx.nd.zeros_like(past_observed_values_agg),
      )
  
      target_related_feat_agg = {
          "past_target": past_target_agg,
          "past_is_pad": past_is_pad_agg,
          "past_observed_values": past_observed_values_agg,
      }
      if future_target is not None:
          future_target_agg = utils.agg_series(
              future_target, agg_multiple=agg_multiple
          ).squeeze(axis=-1)
  
          future_observed_values_agg = utils.agg_series(
              future_observed_values, agg_multiple=agg_multiple
          ).squeeze(axis=-1)
  
          future_observed_values_agg = mx.nd.where(
              future_observed_values_agg == agg_multiple,
              mx.nd.ones_like(future_observed_values_agg),
              mx.nd.zeros_like(future_observed_values_agg),
          )
  
          target_related_feat_agg.update(
              {
                  "future_target": future_target_agg,
                  "future_observed_values": future_observed_values_agg,
              }
          )
  
      return target_related_feat_agg
  
imports: from typing import Dict, List, Optional, Tuple
  import mxnet
  import numpy
  from gluonts.core.component import Type, validated
  from gluonts.itertools import prod
  from gluonts.mx.model.deepar import DeepAREstimator
  from gluonts.mx.model.deepar._network import DeepARPredictionNetwork
  from gluonts.mx.model.deepvar_hierarchical._estimator import projection_mat
  from gluonts.mx.model.deepvar_hierarchical._network import coherency_error
  from gluonts.mx.distribution import Distribution, EmpiricalDistribution
  from gluonts.mx import Tensor
  from gluonts.mx.distribution import TransformedPiecewiseLinear
  from gluonts.nursery.temporal_hierarchical_forecasting.utils import utils
  from gluonts.nursery.temporal_hierarchical_forecasting.model.cop_deepar import gluonts_fixes, gnn
label_desc: Use image node_id 2 for calling the COPNetwork obj's underlying member method code with example usage: obj.get_target_related_feat_at_agg_level(agg_level, past_target, past_observed_values, past_is_pad, future_target, future_observed_values) and returns: target_related_feat_agg