class_docstr: Graph convolution module used by the GraphSAGE model.
  
  Parameters
  ----------
  in_feat : int
      Input feature size.
  out_feat : int
      Output feature size.
class_docstr_tok: ["Graph","convolution","module","used","by","the","GraphSAGE","model",".","Parameters","--","--","--","--","--","in_feat",":","int","Input","feature","size",".","out_feat",":","int","Output","feature","size","."]
code: def __init__(self, in_feat, out_feat):
      super(SAGEConv, self).__init__()
      # A linear submodule for projecting the input and neighbor feature to the output.
      self.linear = nn.Linear(in_feat * 2, out_feat)
  
imports: import os
  import dgl
  import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  import dgl.data
label_desc: Use image node_id 1 to create a new SAGEConv object from inherited base classes: nn with example: obj = SAGEConv(in_feat, out_feat)