code: def compute_eltwise_huber_quantile_loss(y, t, taus):
      """Compute elementwise Huber losses for quantile regression.
  
      This is based on Algorithm 1 of https://arxiv.org/abs/1806.06923.
  
      This function assumes that, both of the two kinds of quantile thresholds,
      taus (used to compute y) and taus_prime (used to compute t) are iid samples
      from U([0,1]).
  
      Args:
          y (torch.Tensor): Quantile prediction from taus as a
              (batch_size, N)-shaped array.
          t (torch.Tensor or ndarray): Target values for quantile regression
              as a (batch_size, N_prime)-array.
          taus (ndarray): Quantile thresholds used to compute y as a
              (batch_size, N)-shaped array.
  
      Returns:
          torch.Tensor: Loss (batch_size, N, N_prime)
      """
      assert y.shape == taus.shape
      # (batch_size, N) -> (batch_size, N, 1)
      y = y.unsqueeze(2)
      # (batch_size, N_prime) -> (batch_size, 1, N_prime)
      t = t.unsqueeze(1)
      # (batch_size, N) -> (batch_size, N, 1)
      taus = taus.unsqueeze(2)
      # Broadcast to (batch_size, N, N_prime)
      y, t, taus = torch.broadcast_tensors(y, t, taus)
      I_delta_lt_0 = (t < y).float()
      eltwise_huber_loss = nn.functional.smooth_l1_loss(
          y, t, reduction="none"
      )
      eltwise_loss = torch.abs(taus - I_delta_lt_0) * eltwise_huber_loss
      return eltwise_loss
  
imports: import numpy
  import torch
  from torch import nn
  from pfrl.action_value import QuantileDiscreteActionValue
  from pfrl.agents import dqn
  from pfrl.nn import Recurrent
  from pfrl.utils.recurrent import one_step_forward, pack_and_forward
label_desc: Use image node_id 3 for calling a global function with example usage: compute_eltwise_huber_quantile_loss(y, t, taus) and returns: eltwise_loss