code: def __init__(
      self,
      prediction_length: int,
      is_energy_score: bool = True,
      threshold_input: float = 100.0,
      es_num_samples: int = 50,
      beta: float = 1.0,
  ) -> None:
      super().__init__(self, beta=beta)
      # A null args_dim to be called by PtArgProj
      self.args_dim = cast(
          Dict[str, int],
          {"null": 1},
      )
  
      self.prediction_length = prediction_length
      self.is_energy_score = is_energy_score
      self.threshold_input = threshold_input
      self.es_num_samples = es_num_samples
  
imports: from typing import Dict, List, Optional, Tuple, cast
  import torch
  from torch.distributions import AffineTransform, TransformedDistribution
  from torch.distributions.normal import Normal
  from gluonts.core.component import validated
  from gluonts.itertools import prod
  from gluonts.torch.distributions import DistributionOutput
  from gluonts.torch.model.mqf2.module import PICNN
label_desc: Use image node_id 1 to create a new MQF2DistributionOutput object from inherited base classes: DistributionOutput with example: obj = MQF2DistributionOutput(prediction_length, is_energy_score, threshold_input, es_num_samples, beta)