class_docstr: The MixHop synthetic dataset from the `"MixHop: Higher-Order
  Graph Convolutional Architectures via Sparsified Neighborhood Mixing"
  <https://arxiv.org/abs/1905.00067>`_ paper, containing 10
  graphs, each with varying degree of homophily (ranging from 0.0 to 0.9).
  All graphs have 5,000 nodes, where each node corresponds to 1 out of 10
  classes.
  The feature values of the nodes are sampled from a 2D Gaussian
  distribution, which are distinct for each class.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      homophily (float): The degree of homophily (one of :obj:`0.0`,
          :obj:`0.1`, ..., :obj:`0.9`).
      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`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
code: def __repr__(self) -> str:
      return (
          f"{self.__class__.__name__}(homophily={self.homophily:.1f})"
      )
  
file_path: pytorch_geometric/torch_geometric/datasets/mixhop_synthetic_dataset.py
imports: import os.path
  import pickle
  from typing import Callable, List, Optional
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url
label_desc: Use image node_id 8 for calling the MixHopSyntheticDataset obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str