class_docstr: Provides an encoder for integer node features.
  
  Args:
      num_classes (int): The number of classes for the embedding mapping to
          learn.
class_docstr_tok: ["Provides","an","encoder","for","integer","node","features",".","Args",":","num_classes","(","int",")",":","The","number","of","classes","for","the","embedding","mapping","to","learn","."]
code: def forward(self, batch):
      # Encode just the first dimension if more exist
      batch.x = self.encoder(batch.x[:, 0])
  
      return batch
  
file_path: pytorch_geometric/graphgym/custom_graphgym/encoder/example.py
imports: import torch
  from ogb.utils.features import get_bond_feature_dims
  from torch_geometric.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