class_docstr: The atom encoder used in OGB molecule dataset.
  
  Args:
      emb_dim (int): The output embedding dimension.
  
  Example:
      >>> encoder = AtomEncoder(emb_dim=16)
      >>> batch = torch.randint(0, 10, (10, 3))
      >>> encoder(batch).size()
      torch.Size([10, 16])
code: def __init__(self, emb_dim, *args, **kwargs):
      super().__init__()
  
      from ogb.utils.features import get_atom_feature_dims
  
      self.atom_embedding_list = torch.nn.ModuleList()
  
      for i, dim in enumerate(get_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: pytorch_geometric/torch_geometric/graphgym/models/encoder.py
imports: import torch
  from torch_geometric.graphgym.register import register_edge_encoder, register_node_encoder
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)