class_docstr: Sampler class that wraps an existing sampler for node classification into another
  one for edge classification or link prediction.
  
  See also
  --------
  as_edge_prediction_sampler
class_docstr_tok: ["Sampler","class","that","wraps","an","existing","sampler","for","node","classification","into","another","one","for","edge","classification","or","link","prediction",".","See","also","--","--","--","--","as_edge_prediction_sampler"]
docstr: Assign lazy features for prefetching.
docstr_tok: ["Assign","lazy","features","for","prefetching","."]
code: def assign_lazy_features(self, result):
      """Assign lazy features for prefetching."""
      pair_graph = result[1]
      set_edge_lazy_features(pair_graph, self.prefetch_labels)
      # In-place updates
      return result
  
imports: import inspect
  from collections.abc import Mapping
  from ..None import backend
  from ..base import EID, NID
  from ..convert import heterograph
  from ..frame import LazyFeature
  from ..transforms import compact_graphs
  from ..utils import context_of, recursive_apply
label_desc: Use image node_id 3 for calling the EdgePredictionSampler obj's underlying member method code with example usage: obj.assign_lazy_features(result) and returns: result