class_docstr:     GRACE model
  Parameters
  -----------
  in_dim: int
      Input feature size.
  hid_dim: int
      Hidden feature size.
  out_dim: int
      Output feature size.
  num_layers: int
      Number of the GNN encoder layers.
  act_fn: nn.Module
      Activation function.
  temp: float
      Temperature constant.
class_docstr_tok: ["GRACE","model","Parameters","--","--","--","--","--","-","in_dim",":","int","Input","feature","size",".","hid_dim",":","int","Hidden","feature","size",".","out_dim",":","int","Output","feature","size",".","num_layers",":","int","Number","of","the","GNN","encoder","layers",".","act_fn",":","nn.Module","Activation","function",".","temp",":","float","Temperature","constant","."]
code: def __init__(
      self, in_dim, hid_dim, out_dim, num_layers, act_fn, temp
  ):
      super(Grace, self).__init__()
      self.encoder = GCN(in_dim, hid_dim, act_fn, num_layers)
      self.temp = temp
      self.proj = MLP(hid_dim, out_dim)
  
file_path: dgl/examples/pytorch/grace/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import GraphConv
label_desc: Use image node_id 1 to create a new Grace object from inherited base classes: nn with example: obj = Grace(in_dim, hid_dim, out_dim, num_layers, act_fn, temp)