class_docstr: Polypharmacy side effect prediction dataset. Only for full-batch training.
  
  Args:
      url (string): The url to download the dataset from.
      root (string): The root directory containing the dataset file.
      name (string): Name of the dataset.
      mode (string): "train", "valid" or "test". Defaults to "train".
class_docstr_tok: ["Polypharmacy","side","effect","prediction","dataset",".","Only","for","full-batch","training",".","Args",":","url","(","string",")",":","The","url","to","download","the","dataset","from",".","root","(","string",")",":","The","root","directory","containing","the","dataset","file",".","name","(","string",")",":","Name","of","the","dataset",".","mode","(","string",")",":","``","train","''",",","``","valid","''","or","``","test","''",".","Defaults","to","``","train","''","."]
code: def __getitem__(self, idx):
      return self.edge_index, self.edge_type, self.edge_type_range
  
file_path: pykale/kale/loaddata/polypharmacy_datasets.py
imports: import os
  import torch
  from torch.utils.data import Dataset
  from torch_geometric.data.data import Data
  from kale.utils.download import download_file_by_url
label_desc: Use image node_id 4 for calling the PolypharmacyDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: self,  self,  self