code: def sampling_decoder(
      self,
      F,
      state_ls,
      scale_ls,
      static_feat_ls,
      past_target_ls,
      future_time_feat_agg_ls,
  ):
      num_windows = (
          self.prediction_length // self.temporal_hierarchy.num_leaves
      )
      num_nodes_per_level = self.temporal_hierarchy.num_nodes_per_level
  
      reconciled_samples_at_all_levels_ls = []
      for j in range(num_windows):
          embeddings_at_all_levels_ls = []
          for i, agg_multiple in enumerate(
              self.temporal_hierarchy.agg_multiples
          ):
              rnn_outputs, states = self._decode_one_window(
                  F=F,
                  model=self.models[i],
                  window_size=num_nodes_per_level[i],
                  offset=j * num_nodes_per_level[i],
                  past_target=past_target_ls[i],
                  time_feat=future_time_feat_agg_ls[i],
                  static_feat=static_feat_ls[i],
                  scale=scale_ls[i],
                  begin_states=state_ls[i],
              )
  
              state_ls[i] = states
              embeddings_at_all_levels_ls.append(
                  rnn_outputs.reshape(
                      (rnn_outputs.shape[0], -1, rnn_outputs.shape[-1])
                  )
              )
  
          # Last dimension contains embeddings at all time-levels and message passing/aggregation can be done on it.
          # Shape: (bs, total_num_time_steps_of_hierarchy, embedding_dim)
          embeddings_at_all_levels = F.concat(
              *embeddings_at_all_levels_ls, dim=-2
          )
  
          if self.use_gnn:
              embeddings_at_all_levels = self.gnn(
                  embeddings_at_all_levels
              )
  
          distr_at_all_levels = self._embeddings_to_distr(
              F,
              embeddings_at_all_levels,
              scale_ls,
          )
  
          samples_at_all_levels = self._distr_to_samples(
              distr_at_all_levels,
              num_samples=1,
          )
  
          if self.do_reconciliation:
              reconciled_samples_at_all_levels = reconcile_samples(
                  reconciliation_mat=self.M,
                  samples=samples_at_all_levels,
                  non_negative=self.non_negative,
              )
          else:
              reconciled_samples_at_all_levels = samples_at_all_levels
  
          rec_err = coherency_error(
              S=self.temporal_hierarchy.agg_mat,
              samples=reconciled_samples_at_all_levels.asnumpy(),
          )
          print(f"Reconciliation error: {rec_err}")
  
          cumsum_nodes_per_level = np.cumsum([0] + num_nodes_per_level)
          for i in range(len(self.temporal_hierarchy.agg_multiples)):
              # (batch_size * num_samples, seq_len, *target_shape)
              reconciled_samples = (
                  reconciled_samples_at_all_levels.slice_axis(
                      axis=-1,
                      begin=cumsum_nodes_per_level[i],
                      end=cumsum_nodes_per_level[i + 1],
                  )
              )
              past_target_ls[i] = F.concat(
                  past_target_ls[i], reconciled_samples, dim=1
              )
  
          reconciled_samples_at_all_levels_ls.append(
              reconciled_samples_at_all_levels.reshape(
                  shape=(
                      -1,
                      self.num_parallel_samples,
                      reconciled_samples_at_all_levels.shape[-1],
                  )
              ).expand_dims(axis=-2)
          )
  
      reconciled_samples_at_all_levels = F.concat(
          *reconciled_samples_at_all_levels_ls, dim=-2
      )
      print(reconciled_samples_at_all_levels.shape)
      return reconciled_samples_at_all_levels
  
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 3 for calling the COPDeepARPredictionNetwork obj's underlying member method code with example usage: obj.sampling_decoder(F, state_ls, scale_ls, static_feat_ls, past_target_ls, future_time_feat_agg_ls) and returns: reconciled_samples_at_all_levels