class_docstr: Provides an encoder for integer node features
  
  Parameters:
  num_classes - the number of classes for the embedding mapping to learn
class_docstr_tok: ["Provides","an","encoder","for","integer","node","features","Parameters",":","num_classes","-","the","number","of","classes","for","the","embedding","mapping","to","learn"]
code: def __init__(self, emb_dim, num_classes=None):
      super(IntegerFeatureEncoder, self).__init__()
  
      self.encoder = torch.nn.Embedding(num_classes, emb_dim)
      torch.nn.init.xavier_uniform_(self.encoder.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 IntegerFeatureEncoder object from inherited base classes: torch.nn with example: obj = IntegerFeatureEncoder(emb_dim, num_classes)