class_docstr: The complete Atom Encoder used in OGB dataset
  
  Parameters:
  num_classes: Not used!
class_docstr_tok: ["The","complete","Atom","Encoder","used","in","OGB","dataset","Parameters",":","num_classes",":","Not","used","!"]
code: def __init__(self, emb_dim, num_classes=None):
      super(AtomEncoder, self).__init__()
  
      self.atom_embedding_list = torch.nn.ModuleList()
  
      for i, dim in enumerate(full_atom_feature_dims):
          emb = torch.nn.Embedding(dim, emb_dim)
          torch.nn.init.xavier_uniform_(emb.weight.data)
          self.atom_embedding_list.append(emb)
  
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 AtomEncoder object from inherited base classes: torch.nn with example: obj = AtomEncoder(emb_dim, num_classes)