class_docstr: See arXiv: 1806.06923
  This module, in combination with quantile loss,
  learns how to generate the quantile of the distribution of the target.
  A quantile value, tau, is randomly generated with a Uniform([0, 1])).
  This quantile value is embedded in this module and also passed to the quantile loss:
  this should force the model to learn the appropriate quantile.
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code: def __init__(self, in_features, output_domain_cls):
      super(ImplicitQuantileModule, self).__init__()
      self.in_features = in_features
      self.quantile_layer = QuantileLayer(in_features)
      self.output_layer = nn.Sequential(
          nn.Linear(in_features, in_features),
          nn.Softplus(),
          nn.Linear(in_features, 1),
          output_domain_cls(),
      )
  
file_path: gluonts/src/gluonts/nursery/robust-mts-attack/pts/modules/iqn_modules.py
imports: from math import pi
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new ImplicitQuantileModule object from inherited base classes: nn with example: obj = ImplicitQuantileModule(in_features, output_domain_cls)