class_docstr: Only encode the first dimension of atom integer features.
  This feature encodes just the atom type
  
  Parameters:
  num_classes: Not used!
class_docstr_tok: ["Only","encode","the","first","dimension","of","atom","integer","features",".","This","feature","encodes","just","the","atom","type","Parameters",":","num_classes",":","Not","used","!"]
code: def __init__(self, emb_dim, num_classes=None):
      super(SingleAtomEncoder, self).__init__()
  
      num_atom_types = full_atom_feature_dims[0]
      self.atom_type_embedding = torch.nn.Embedding(
          num_atom_types, emb_dim
      )
      torch.nn.init.xavier_uniform_(
          self.atom_type_embedding.weight.data
      )
  
file_path: GraphGym/graphgym/models/feature_encoder_pyg.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 1 to create a new SingleAtomEncoder object from inherited base classes: torch.nn with example: obj = SingleAtomEncoder(emb_dim, num_classes)