class_docstr: An abstract task which consists of meta information and
  Train/Validation/Test Set.
  
  * meta information
      The meta information of a task includes any kinds of data that are
      defined by the user in YAML when instantiating the task.
  
  * Train/Validation/Test Set
      The train/validation/test (TVT) set which is used to train the neural
      networks. We calculate the embeddings based on their respective features
      and the graph structure, and then utilize the embeddings to optimize the
      neural network parameters.
class_docstr_tok: ["An","abstract","task","which","consists","of","meta","information","and","Train\/Validation\/Test","Set",".","*","meta","information","The","meta","information","of","a","task","includes","any","kinds","of","data","that","are","defined","by","the","user","in","YAML","when","instantiating","the","task",".","*","Train\/Validation\/Test","Set","The","train\/validation\/test","(","TVT",")","set","which","is","used","to","train","the","neural","networks",".","We","calculate","the","embeddings","based","on","their","respective","features","and","the","graph","structure",",","and","then","utilize","the","embeddings","to","optimize","the","neural","network","parameters","."]
docstr: Return the validation set.
docstr_tok: ["Return","the","validation","set","."]
code: def validation_set(self) -> Union[ItemSet, ItemSetDict]:
      """Return the validation set."""
      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 3 for calling the Task obj's underlying member method code with example usage: obj.validation_set() without return types