class_docstr: An abstract dataset which provides abstraction for accessing the data
  required for training.
  
  The data abstraction could be a native CPU memory block, a shared memory
  block, a file handle of an opened file on disk, a service that provides
  the API to access the data e.t.c. There are 3 primary components in the
  dataset:
  
  * Task
      A task consists of several meta information and the
      Train/Validation/Test Set. A dataset could have multiple tasks.
  
  * Feature Storage
      A key-value store which stores node/edge/graph features.
  
  * Graph Topology
      Graph topology is used by the subgraph sampling algorithm to generate
      a subgraph.
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docstr: Return the tasks.
docstr_tok: ["Return","the","tasks","."]
code: def tasks(self) -> List[Task]:
      """Return the tasks."""
      raise NotImplementedError
  
file_path: dgl/python/dgl/graphbolt/dataset.py
imports: from typing import Dict, List, Union
  from .feature_store import FeatureStore
  from .itemset import ItemSet, ItemSetDict
  from .sampling_graph import SamplingGraph
label_desc: Use image node_id 1 for calling the Dataset obj's underlying member method code with example usage: obj.tasks() without return types