code: def reconcile_samples(
      reconciliation_mat: Tensor,
      samples: Tensor,
      non_negative: bool = False,
      num_iters: int = 10,
  ) -> Tensor:
      if not non_negative:
          return mx.nd.dot(
              samples, reconciliation_mat, transpose_b=True
          )
      else:
          # Dykstra's projection method: Projection onto the intersection of convex sets.
          x = samples
          p = mx.nd.zeros_like(x)
          q = mx.nd.zeros_like(x)
          for _ in range(num_iters):
              # Projection onto the non-negative orthant.
              y = mx.nd.relu(x + p)
              p = x + p - y
  
              # Projection onto the null space.
              x = mx.nd.dot(y + q, reconciliation_mat, transpose_b=True)
              q = y + q - x
  
          return x
  
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 for calling a global function with example usage: reconcile_samples(reconciliation_mat, samples, non_negative, num_iters) and returns: mx, x