class_docstr: AutoNE HPOptimizer
  The Implementation of "AutoNE: Hyperparameter Optimization for Massive Network Embedding"(KDD 2019).
  See https://github.com/tadpole/AutoNE for more information
  
  Attributes
  ----------
  max_evals : int
      The max rounds of evaluating HPs
  subgraphs : int
      The number of subgraphs
  sub_evals : int
      The number of evaluation times on each subgraph
  sample_batch_size, sample_walk_length : int
      Using for sampling subgraph, see torch_geometric.data.GraphSAINRandomWalkSampler
docstr: Build a new hpo instance.
docstr_tok: ["Build","a","new","hpo","instance","."]
code: def build_hpo_from_args(cls, args):
      """Build a new hpo instance."""
      raise NotImplementedError(
          "HP Optimizer must implement the build_hpo_from_args method"
      )
  
file_path: AutoGL/autogl/module/hpo/autone.py
imports: import time
  import numpy
  from tqdm import trange
  from .None import register_hpo
  from .base import BaseHPOptimizer, TimeTooLimitedError
  import random
  from .autone_file import utils
  from ..feature import NetLSD
  from autogl.backend import DependentBackend
label_desc: Use image node_id 3 for calling the AutoNE obj's underlying member method code with example usage: obj.build_hpo_from_args(cls, args) without return types