class_docstr: Define quantile embedding layer, i.e. phi in the IQN paper (arXiv: 1806.06923).
class_docstr_tok: ["Define","quantile","embedding","layer",",","i.e",".","phi","in","the","IQN","paper","(","arXiv",":","1806.06923",")","."]
code: def __init__(self, num_output):
      super(QuantileLayer, self).__init__()
      self.n_cos_embedding = 64
      self.num_output = num_output
      self.output_layer = nn.Sequential(
          nn.Linear(self.n_cos_embedding, self.n_cos_embedding),
          nn.PReLU(),
          nn.Linear(self.n_cos_embedding, num_output),
      )
  
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 QuantileLayer object from inherited base classes: nn with example: obj = QuantileLayer(num_output)