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 __init__(self, emb_dim, num_classes=None):
      super().__init__()
  
      self.encoder = torch.nn.Embedding(num_classes, emb_dim)
      torch.nn.init.xavier_uniform_(self.encoder.weight.data)
  
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 1 to create a new ExampleNodeEncoder object from inherited base classes: torch.nn with example: obj = ExampleNodeEncoder(emb_dim, num_classes)