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.
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 argparse
  import pickle
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader, IterableDataset
  from ..None import utils
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)