class_docstr: The Benchmark Data Sets for Graph Kernels from TU Dortmund
  ([link](https://chrsmrrs.github.io/datasets/docs/datasets/)).
  
  Node features are computed by concatenating the following features for
  each node:
  
  - node attributes, if available;
  - node labels, if available, one-hot encoded.
  
  Some datasets might not have node features at all. In this case, attempting
  to use the dataset with a Loader will result in a crash. You can create
  node features using some of the transforms available in `spektral.transforms`
  or you can define your own features by accessing the individual samples in
  the `graph` attribute of the dataset (which is a list of `Graph` objects).
  
  Edge features are computed by concatenating the following features for
  each node:
  
  - edge attributes, if available;
  - edge labels, if available, one-hot encoded.
  
  Graph labels are provided for each dataset.
  
  Specific details about each individual dataset can be found in
  `~/spektral/datasets/TUDataset/<dataset name>/README.md`, after the dataset
  has been downloaded locally (datasets are downloaded automatically upon
  calling `TUDataset('<dataset name>')` the first time).
  
  **Arguments**
  
  - `name`: str, name of the dataset to load (see `TUD.available_datasets`).
  - `clean`: if `True`, rload a version of the dataset with no isomorphic
             graphs.
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code: def available_datasets():
      url = "https://chrsmrrs.github.io/datasets/docs/datasets/"
      try:
          tables = pd.read_html(url)
          names = []
          for table in tables:
              names.extend(table.Name[1:].values.tolist())
          return names
      except URLError:
          # No internet, don't panic
          print("Could not read URL {}".format(url))
          return []
  
file_path: spektral/spektral/datasets/tudataset.py
imports: import glob
  import os
  import shutil
  from os import path
  from urllib.error import URLError
  import numpy
  import pandas
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from spektral.data import Dataset, Graph
  from spektral.datasets.utils import download_file
  from spektral.utils import io, sparse
label_desc: Use image node_id 5 for calling the TUDataset obj's underlying member method code with example usage: obj.available_datasets() and returns: names, list