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 forward(self, tau):
      cos_embedded_tau = self.cos_embed(tau)
      final_output = self.output_layer(cos_embedded_tau)
      return final_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 2 for calling the QuantileLayer obj's underlying member method code with example usage: obj.forward(tau) and returns: final_output