class_docstr: Provides an encoder for integer node features.
  
  Args:
      emb_dim (int): Output embedding dimension
      num_classes (int): the number of classes for the
      embedding mapping to learn from
class_docstr_tok: ["Provides","an","encoder","for","integer","node","features",".","Args",":","emb_dim","(","int",")",":","Output","embedding","dimension","num_classes","(","int",")",":","the","number","of","classes","for","the","embedding","mapping","to","learn","from"]
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/models/feature_encoder.py
imports: import torch
  from ogb.utils.features import get_atom_feature_dims, get_bond_feature_dims
  import graphgym.register
label_desc: Use image node_id 2 for calling the IntegerFeatureEncoder obj's underlying member method code with example usage: obj.forward(batch) and returns: batch