class_docstr: A `Pytorch Geometric <https://pytorch-geometric.readthedocs.io/en/latest/index.html>`_ data interface for :obj:`MD17` dataset 
  which is from `"Machine learning of accurate energy-conserving molecular force fields" <https://advances.sciencemag.org/content/3/5/e1603015.short>`_ paper. 
  MD17 is a collection of eight molecular dynamics simulations for small organic molecules. 
  
  Args:
      root (string): The dataset folder will be located at root/name.
      name (string): The name of dataset. Available dataset names are as follows: :obj:`aspirin`, :obj:`benzene_old`, :obj:`ethanol`, :obj:`malonaldehyde`, 
          :obj:`naphthalene`, :obj:`salicylic`, :obj:`toluene`, :obj:`uracil`. (default: :obj:`benzene_old`)
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
  
  Example:
  --------
  
  >>> dataset = MD17(name='aspirin')
  >>> split_idx = dataset.get_idx_split(len(dataset.data.y), train_size=1000, valid_size=1000, seed=42)
  >>> train_dataset, valid_dataset, test_dataset = dataset[split_idx['train']], dataset[split_idx['valid']], dataset[split_idx['test']]
  >>> train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
  >>> data = next(iter(train_loader))
  >>> data
  Batch(batch=[672], force=[672, 3], pos=[672, 3], ptr=[33], y=[32], z=[672])
  
  Where the attributes of the output data indicates:
  
  * :obj:`z`: The atom type.
  * :obj:`pos`: The 3D position for atoms.
  * :obj:`y`: The property (energy) for the graph (molecule).
  * :obj:`force`: The 3D force for atoms.
  * :obj:`batch`: The assignment vector which maps each node to its respective graph identifier and can help reconstructe single graphs
class_docstr_tok: ["A","`","Pytorch","Geometric","<","https",":","\/\/pytorch-geometric.readthedocs.io\/en\/latest\/index.html",">","`","_","data","interface","for",":","obj",":","`","MD17","`","dataset","which","is","from","`","``","Machine","learning","of","accurate","energy-conserving","molecular","force","fields","''","<","https",":","\/\/advances.sciencemag.org\/content\/3\/5\/e1603015.short",">","`","_","paper",".","MD17","is","a","collection","of","eight","molecular","dynamics","simulations","for","small","organic","molecules",".","Args",":","root","(","string",")",":","The","dataset","folder","will","be","located","at","root\/name",".","name","(","string",")",":","The","name","of","dataset",".","Available","dataset","names","are","as","follows",":",":","obj",":","`","aspirin","`",",",":","obj",":","`","benzene_old","`",",",":","obj",":","`","ethanol","`",",",":","obj",":","`","malonaldehyde","`",",",":","obj",":","`","naphthalene","`",",",":","obj",":","`","salicylic","`",",",":","obj",":","`","toluene","`",",",":","obj",":","`","uracil","`",".","(","default",":",":","obj",":","`","benzene_old","`",")","transform","(","callable",",","optional",")",":","A","function\/transform","that","takes","in","an",":","obj",":","`","torch_geometric.data.Data","`","object","and","returns","a","transformed","version",".","The","data","object","will","be","transformed","before","every","access",".","(","default",":",":","obj",":","`","None","`",")","pre_transform","(","callable",",","optional",")",":","A","function\/transform","that","takes","in","an",":","obj",":","`","torch_geometric.data.Data","`","object","and","returns","a","transformed","version",".","The","data","object","will","be","transformed","before","being","saved","to","disk",".","(","default",":",":","obj",":","`","None","`",")","pre_filter","(","callable",",","optional",")",":","A","function","that","takes","in","an",":","obj",":","`","torch_geometric.data.Data","`","object","and","returns","a","boolean","value",",","indicating","whether","the","data","object","should","be","included","in","the","final","dataset",".","(","default",":",":","obj",":","`","None","`",")","Example",":","--","--","--","--",">",">",">","dataset","=","MD17","(","name='aspirin","'",")",">",">",">","split_idx","=","dataset.get_idx_split","(","len","(","dataset.data.y",")",",","train_size=1000",",","valid_size=1000",",","seed=42",")",">",">",">","train_dataset",",","valid_dataset",",","test_dataset","=","dataset","[","split_idx","[","'train","'","]","]",",","dataset","[","split_idx","[","'valid","'","]","]",",","dataset","[","split_idx","[","'test","'","]","]",">",">",">","train_loader","=","DataLoader","(","train_dataset",",","batch_size=32",",","shuffle=True",")",">",">",">","data","=","next","(","iter","(","train_loader",")",")",">",">",">","data","Batch","(","batch=","[","672","]",",","force=","[","672",",","3","]",",","pos=","[","672",",","3","]",",","ptr=","[","33","]",",","y=","[","32","]",",","z=","[","672","]",")","Where","the","attributes","of","the","output","data","indicates",":","*",":","obj",":","`","z","`",":","The","atom","type",".","*",":","obj",":","`","pos","`",":","The","3D","position","for","atoms",".","*",":","obj",":","`","y","`",":","The","property","(","energy",")","for","the","graph","(","molecule",")",".","*",":","obj",":","`","force","`",":","The","3D","force","for","atoms",".","*",":","obj",":","`","batch","`",":","The","assignment","vector","which","maps","each","node","to","its","respective","graph","identifier","and","can","help","reconstructe","single","graphs"]
code: def download(self):
      download_url(self.url, self.raw_dir)
  
file_path: DIG/dig/threedgraph/dataset/PygMD17.py
imports: import os.path
  import numpy
  from tqdm import tqdm
  import torch
  from sklearn.utils import shuffle
  from torch_geometric.data import InMemoryDataset, download_url
  from torch_geometric.data import Data, DataLoader
label_desc: Use image node_id 4 for calling the MD17 obj's underlying member method code with example usage: obj.download() without return types