docstr: g : DGLGraph
      The user-item interaction graph.
      This is only for finding the range of categorical variables.
  item_textsets : torchtext.data.Dataset
      The textual features of each item node.
docstr_tok: ["g",":","DGLGraph","The","user-item","interaction","graph",".","This","is","only","for","finding","the","range","of","categorical","variables",".","item_textsets",":","torchtext.data.Dataset","The","textual","features","of","each","item","node","."]
code: def __init__(self, hidden_dims, n_layers):
      """
      g : DGLGraph
          The user-item interaction graph.
          This is only for finding the range of categorical variables.
      item_textsets : torchtext.data.Dataset
          The textual features of each item node.
      """
      super().__init__()
  
      self.convs = nn.ModuleList()
      for _ in range(n_layers):
          self.convs.append(
              WeightedSAGEConv(hidden_dims, hidden_dims, hidden_dims)
          )
  
imports: import dgl
  import dgl.function
  import dgl.nn.pytorch
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new SAGENet object from inherited base classes: nn with example: obj = SAGENet(hidden_dims, n_layers)