code: def _distr_to_samples(
      self,
      distr_at_all_levels: Distribution,
      num_samples: int,
  ):
      if num_samples == 1:
          samples_at_all_levels = distr_at_all_levels.sample(
              num_samples=num_samples, dtype=self.dtype
          )
  
          # get rid of the redundant axis introduced by `sample`.
          samples_at_all_levels = samples_at_all_levels.squeeze(axis=0)
      else:
          samples_at_all_levels = distr_at_all_levels.sample_rep(
              num_samples=num_samples, dtype=self.dtype
          )
  
      return 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 4 for calling the COPNetwork obj's underlying member method code with example usage: obj._distr_to_samples(distr_at_all_levels, num_samples) and returns: samples_at_all_levels