code: def test(self):
      batch_size = self.batch_size
      N = self.N
      N_prime = self.N_prime
      huber_loss_threshold = self.huber_loss_threshold
  
      # Overestimation is penalized proportionally to tau
      # Underestimation is penalized proportionally to (1-tau)
      y = np.random.normal(size=(batch_size, N)).astype("f")
      y_var = chainer.Variable(y)
      t = np.random.normal(size=(batch_size, N_prime)).astype("f")
      tau = np.random.uniform(size=(batch_size, N)).astype("f")
  
      loss = iqn.compute_eltwise_huber_quantile_loss(
          y_var, t, tau, huber_loss_threshold=huber_loss_threshold
      )
      y_var_b, t_b = F.broadcast(
          F.reshape(y_var, (batch_size, N, 1)),
          F.reshape(t, (batch_size, 1, N_prime)),
      )
      self.assertEqual(loss.shape, (batch_size, N, N_prime))
      huber_loss = F.huber_loss(
          y_var_b, t_b, delta=huber_loss_threshold, reduce="no"
      )
      self.assertEqual(huber_loss.shape, (batch_size, N, N_prime))
  
      for i in range(batch_size):
          for j in range(N):
              for k in range(N_prime):
                  # loss is always positive
                  scalar_loss = loss[i, j, k]
                  scalar_grad = chainer.grad([scalar_loss], [y_var])[0][
                      i, j
                  ]
                  self.assertGreater(scalar_loss.array, 0)
                  if y[i, j] > t[i, k]:
                      # y over-estimates t
                      # loss equals huber loss scaled by tau
                      correct_scalar_loss = (
                          tau[i, j] * huber_loss[i, j, k]
                      )
                  else:
                      # y under-estimates t
                      # loss equals huber loss scaled by (1-tau)
                      correct_scalar_loss = (
                          1 - tau[i, j]
                      ) * huber_loss[i, j, k]
                  correct_scalar_grad = chainer.grad(
                      [correct_scalar_loss], [y_var]
                  )[0][i, j]
                  self.assertAlmostEqual(
                      scalar_loss.array,
                      correct_scalar_loss.array,
                      places=5,
                  )
                  self.assertAlmostEqual(
                      scalar_grad.array,
                      correct_scalar_grad.array,
                      places=5,
                  )
  
imports: import unittest
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import testing
  import basetest_dqn_like
  from basetest_training import _TestBatchTrainingMixin
  import chainerrl
  from chainerrl.agents import iqn
label_desc: Use image node_id 1 for calling the TestComputeEltwiseHuberQuantileLoss obj's underlying member method code with example usage: obj.test() without return types