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 sim(self, z1, z2):
      # normalize embeddings across feature dimension
      z1 = F.normalize(z1)
      z2 = F.normalize(z2)
  
      s = th.mm(z1, z2.t())
      return s
  
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 2 for calling the Grace obj's underlying member method code with example usage: obj.sim(z1, z2) and returns: s