class_docstr: Provides an encoder for integer node features.
  
  Args:
      emb_dim (int): Output embedding dimension
      num_classes (int): the number of classes for the
      embedding mapping to learn from
class_docstr_tok: ["Provides","an","encoder","for","integer","node","features",".","Args",":","emb_dim","(","int",")",":","Output","embedding","dimension","num_classes","(","int",")",":","the","number","of","classes","for","the","embedding","mapping","to","learn","from"]
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.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)