class_docstr: Provides an encoder for integer node features
  
  Parameters:
  num_classes - the number of classes for the embedding mapping to learn
class_docstr_tok: ["Provides","an","encoder","for","integer","node","features","Parameters",":","num_classes","-","the","number","of","classes","for","the","embedding","mapping","to","learn"]
code: def forward(self, batch):
      # Encode just the first dimension if more exist
      batch.node_feature = self.encoder(batch.node_feature[:, 0])
  
      return batch
  
file_path: GraphGym/graphgym/contrib/feature_encoder/example.py
imports: import torch
  from ogb.utils.features import get_bond_feature_dims
  from graphgym.register import register_edge_encoder, register_node_encoder
label_desc: Use image node_id 2 for calling the ExampleNodeEncoder obj's underlying member method code with example usage: obj.forward(batch) and returns: batch