class_docstr: Provides an encoder for integer node features.
  
  Args:
      emb_dim (int): The output embedding dimension.
      num_classes (int): The number of classes/integers.
  
  Example:
      >>> encoder = IntegerFeatureEncoder(emb_dim=16, num_classes=10)
      >>> batch = torch.randint(0, 10, (10, 2))
      >>> encoder(batch).size()
      torch.Size([10, 16])
code: def __init__(self, emb_dim: int, num_classes: int):
      super().__init__()
  
      self.encoder = torch.nn.Embedding(num_classes, emb_dim)
      torch.nn.init.xavier_uniform_(self.encoder.weight.data)
  
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 IntegerFeatureEncoder object from inherited base classes: torch.nn with example: obj = IntegerFeatureEncoder(emb_dim, num_classes)