code: def quantile_loss(true, pred, quantile):
      denom = np.abs(true).sum(1).sum(1)  # batch x 1
      if true.ndim < 3:
          true = true.reshape(true.shape + tuple([1] * (3 - true.ndim)))
      if pred.ndim < 3:
          pred = pred.reshape(pred.shape + tuple([1] * (3 - pred.ndim)))
      batch, time, dim = true.shape
      ql = np.zeros(batch)
      idx = 0
      for y_hat, y in zip(pred, true):
          num = 0
          for t in range(time):
              for j in range(dim):
                  num += (
                      (1 - quantile) * abs(y_hat[t, j] - y[t, j])
                      if y_hat[t, j] > y[t, j]
                      else quantile * abs(y_hat[t, j] - y[t, j])
                  )
          ql[idx] = num
          idx += 1
      denom[denom == 0] = 1
      if (denom != 0).any():
          return (2 * ql / denom).reshape(batch, 1, 1)
      else:
          return None
  
imports: import json
  import pickle
  import random
  import torch
  import numpy
  import pandas
  from tqdm.auto import tqdm
  from typing import Iterator
  from gluonts.dataset.common import DataEntry, Dataset, ListDataset
label_desc: Use image node_id 8 for calling a global function with example usage: quantile_loss(true, pred, quantile) and returns: unknown, None