class_docstr: Implements multi-step prediction given a trained `DeepNPTSNetwork` model
  that outputs one-step-ahead forecast distribution.
class_docstr_tok: ["Implements","multi-step","prediction","given","a","trained","`","DeepNPTSNetwork","`","model","that","outputs","one-step-ahead","forecast","distribution","."]
code: def __init__(
      self,
      net: DeepNPTSNetwork,
      prediction_length: int,
      num_parallel_samples: int = 100,
  ):
      super().__init__()
      self.net = net
      self.prediction_length = prediction_length
      self.num_parallel_samples = num_parallel_samples
  
imports: from functools import partial
  from typing import Optional, Callable, List, Union
  import torch
  from torch import nn
  from torch.distributions import Categorical, MixtureSameFamily, Normal
  from gluonts.core.component import validated
  from gluonts.torch.distributions import DiscreteDistribution
  from .scaling import min_max_scaling, standard_normal_scaling
label_desc: Use image node_id 1 to create a new DeepNPTSMultiStepNetwork object from inherited base classes: nn with example: obj = DeepNPTSMultiStepNetwork(net, prediction_length, num_parallel_samples)