code: def forward(self, q: torch.Tensor, g: torch.Tensor):
      n = q.shape[-1]
      tau = torch.arange(0, n).unsqueeze(0).to(q.device) / n
      h = self.huber_loss(g, q)
      k = torch.abs(tau - (g - q > 0).float())
      return torch.mean(h * k)
  
file_path: nebuly/optimization/open_alpha_tensor/open_alpha_tensor/core/modules/extras.py
imports: import torch
label_desc: Use image node_id 2 for calling the QuantileLoss obj's underlying member method code with example usage: obj.forward(q, g) and returns: torch