class_docstr: AutoTopkpool. The model used in this automodel is from https://arxiv.org/abs/1905.05178, https://arxiv.org/abs/1905.02850
  
  Parameters
  ----------
  num_features: `int`.
      The dimension of features.
  
  num_classes: `int`.
      The number of classes.
  
  device: `torch.device` or `str`
      The device where model will be running on.
  
  init: `bool`.
      If True(False), the model will (not) be initialized.
class_docstr_tok: ["AutoTopkpool",".","The","model","used","in","this","automodel","is","from","https",":","\/\/arxiv.org\/abs\/1905.05178",",","https",":","\/\/arxiv.org\/abs\/1905.02850","Parameters","--","--","--","--","--","num_features",":","`","int","`",".","The","dimension","of","features",".","num_classes",":","`","int","`",".","The","number","of","classes",".","device",":","`","torch.device","`","or","`","str","`","The","device","where","model","will","be","running","on",".","init",":","`","bool","`",".","If","True","(","False",")",",","the","model","will","(","not",")","be","initialized","."]
code: def from_hyper_parameter(self, hp, **kwargs):
      return super().from_hyper_parameter(
          hp, num_graph_features=self.num_graph_features, **kwargs
      )
  
file_path: AutoGL/autogl/module/model/pyg/topkpool.py
imports: import torch
  import torch.nn.functional
  from torch_geometric.nn import GraphConv, TopKPooling
  from torch_geometric.nn import global_mean_pool, global_max_pool
  from .None import register_model
  from .base import BaseAutoModel, activate_func
  from ....utils import get_logger
label_desc: Use image node_id 2 for calling the AutoTopkpool obj's underlying member method code with example usage: obj.from_hyper_parameter(hp) and returns: super