code: def __init__(
      self,
      return_forecasts_at_all_levels: bool = False,
      num_parallel_samples: int = 100,
      **kwargs,
  ) -> None:
      super().__init__(prediction=True, **kwargs)
      self.return_forecasts_at_all_levels = (
          return_forecasts_at_all_levels
      )
      self.num_parallel_samples = num_parallel_samples
  
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 1 to create a new COPDeepARPredictionNetwork object from inherited base classes: COPNetwork with example: obj = COPDeepARPredictionNetwork(return_forecasts_at_all_levels, num_parallel_samples)