class_docstr: Multivariate quantile loss based on the article
  `Multivariate Quantile Function Forecaster <http://arxiv.org/abs/2202.11316>`_.
  
  Requires install of additional library:
  ``pip install pytorch-forecasting[mqf2]``
class_docstr_tok: ["Multivariate","quantile","loss","based","on","the","article","`","Multivariate","Quantile","Function","Forecaster","<","http",":","\/\/arxiv.org\/abs\/2202.11316",">","`","_",".","Requires","install","of","additional","library",":","``","pip","install","pytorch-forecasting","[","mqf2","]","``"]
code: def is_energy_score(self) -> bool:
      return self.es_num_samples is not None
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  from sklearn.base import BaseEstimator
  import torch
  from torch import distributions, nn
  import torch.nn.functional
  from pytorch_forecasting.data.encoders import TorchNormalizer, softplus_inv
  from pytorch_forecasting.metrics.base_metrics import DistributionLoss, MultivariateDistributionLoss
label_desc: Use image node_id 2 for calling the MQF2DistributionLoss obj's underlying member method code with example usage: obj.is_energy_score() and returns: unknown