code: def compute_eltwise_huber_quantile_loss(
      y, t, taus, huber_loss_threshold=1.0
  ):
      """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 (chainer.Variable): Quantile prediction from taus as a
              (batch_size, N)-shaped array.
          t (chainer.Variable 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.
          huber_loss_threshold (float): Threshold of Huber loss. In the IQN
              paper, this is denoted by kappa.
  
      Returns:
          chainer.Variable: Loss (batch_size, N, N_prime)
      """
      assert y.shape == taus.shape
      # (batch_size, N) -> (batch_size, N, 1)
      y = F.expand_dims(y, axis=2)
      # (batch_size, N_prime) -> (batch_size, 1, N_prime)
      t = F.expand_dims(t, axis=1)
      # (batch_size, N) -> (batch_size, N, 1)
      taus = F.expand_dims(taus, axis=2)
      # Broadcast to (batch_size, N, N_prime)
      y, t, taus = F.broadcast(y, t, taus)
      I_delta = ((t.array - y.array) > 0).astype("f")
      eltwise_huber_loss = F.huber_loss(
          y, t, delta=huber_loss_threshold, reduce="no"
      )
      eltwise_loss = abs(taus - I_delta) * eltwise_huber_loss
      return eltwise_loss
  
imports: import chainer
  from chainer import cuda
  import chainer.functions
  import chainer.links
  from chainerrl.action_value import QuantileDiscreteActionValue
  from chainerrl.agents import dqn
  from chainerrl.links import StatelessRecurrentChainList
label_desc: Use image node_id 4 for calling a global function with example usage: compute_eltwise_huber_quantile_loss(y, t, taus, huber_loss_threshold) and returns: eltwise_loss