class_docstr: Inductive NodePiece with a GNN encoder on top.
  
  Overall, it's a 3-step procedure:
  
  1. Featurizing nodes via NodePiece
  2. Message passing over the active graph using NodePiece features
  3. Scoring function for a given batch of triples
  
  As of now, message passing is expected to be over the full graph
class_docstr_tok: ["Inductive","NodePiece","with","a","GNN","encoder","on","top",".","Overall",",","it","'s","a","3-step","procedure",":","1",".","Featurizing","nodes","via","NodePiece","2",".","Message","passing","over","the","active","graph","using","NodePiece","features","3",".","Scoring","function","for","a","given","batch","of","triples","As","of","now",",","message","passing","is","expected","to","be","over","the","full","graph"]
docstr: Reset the GNN encoder explicitly in addition to other params.
docstr_tok: ["Reset","the","GNN","encoder","explicitly","in","addition","to","other","params","."]
code: def reset_parameters_(self):
      """Reset the GNN encoder explicitly in addition to other params."""
      super().reset_parameters_()
      if getattr(self, "gnn_encoder", None) is not None:
          for layer in self.gnn_encoder:
              if hasattr(layer, "reset_parameters"):
                  layer.reset_parameters()
  
file_path: pykeen/src/pykeen/models/inductive/inductive_nodepiece_gnn.py
imports: import logging
  from typing import Iterable, Optional, Tuple, cast
  import torch
  from torch import nn
  from .inductive_nodepiece import InductiveNodePiece
  from ...nn.representation import CompGCNLayer
  from ...typing import HeadRepresentation, InductiveMode, RelationRepresentation, TailRepresentation
  from ...utils import get_edge_index
label_desc: Use image node_id 2 for calling the InductiveNodePieceGNN obj's underlying member method code with example usage: obj.reset_parameters_() without return types