code: def load_all_dataset(dataset: Dataset, num_workers: int = 0):
      """
      Retrieves the contents of a whole dataset by using a DataLoader
  
      :param dataset: The dataset
      :param num_workers: The number of workers the DataLoader should use.
          Defaults to 0.
      :return: The content of the whole Dataset
      """
      # DataLoader parallelism is batch-based. By using "len(dataset)/num_workers"
      # as the batch size, num_workers [+1] batches will be loaded thus
      # using the required number of workers.
      if num_workers > 0:
          batch_size = max(1, len(dataset) // num_workers)
      else:
          batch_size = len(dataset)
      collate_fn = (
          dataset.collate_fn if hasattr(dataset, "collate_fn") else None
      )
      loader = DataLoader(
          dataset,
          batch_size=batch_size,
          drop_last=False,
          num_workers=num_workers,
          collate_fn=collate_fn,
      )
      has_task_labels = False
      batches_x = []
      batches_y = []
      batches_t = []
      for loaded_element in loader:
          batches_x.append(loaded_element[0])
          batches_y.append(loaded_element[1])
          if len(loaded_element) > 2:
              has_task_labels = True
              batches_t.append(loaded_element[2])
  
      x, y = torch.cat(batches_x), torch.cat(batches_y)
  
      if has_task_labels:
          t = torch.cat(batches_t)
          return x, y, t
      else:
          return x, y
  
imports: from collections import defaultdict
  from typing import Callable, Dict, List, NamedTuple, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import Linear, Module
  from torch.utils.data import DataLoader, Dataset
  from avalanche.benchmarks import OnlineCLExperience
  from avalanche.models.batch_renorm import BatchRenorm2D
label_desc: Use image node_id 4 for calling a global function with example usage: load_all_dataset(dataset, num_workers) and returns: x,  y,  t, x,  y