class_docstr: A basic feature store to manage multiple features for access.
class_docstr_tok: ["A","basic","feature","store","to","manage","multiple","features","for","access","."]
docstr: Initiate a basic feature store.
  
  
  Parameters
  ----------
  features : Dict[Tuple[str, str, str], Feature]
      The dict of features served by the feature store, in which the key
      is tuple of (domain, type_name, feature_name).
  
  Returns
  -------
  The feature stores.
docstr_tok: ["Initiate","a","basic","feature","store",".","Parameters","--","--","--","--","--","features",":","Dict","[","Tuple","[","str",",","str",",","str","]",",","Feature","]","The","dict","of","features","served","by","the","feature","store",",","in","which","the","key","is","tuple","of","(","domain",",","type_name",",","feature_name",")",".","Returns","--","--","--","-","The","feature","stores","."]
code: def __init__(self, features: Dict[Tuple[str, str, str], Feature]):
      r"""Initiate a basic feature store.
  
  
      Parameters
      ----------
      features : Dict[Tuple[str, str, str], Feature]
          The dict of features served by the feature store, in which the key
          is tuple of (domain, type_name, feature_name).
  
      Returns
      -------
      The feature stores.
      """
      super().__init__()
      self._features = features
  
file_path: dgl/python/dgl/graphbolt/impl/basic_feature_store.py
imports: from typing import Dict, Tuple
  import torch
  from ..feature_store import Feature, FeatureStore
label_desc: Use image node_id 1 to create a new BasicFeatureStore object from inherited base classes: FeatureStore with example: obj = BasicFeatureStore(features)