class_docstr: The Graph U-Net model from the `"Graph U-Nets"
  <https://arxiv.org/abs/1905.05178>`_ paper which implements a U-Net like
  architecture with graph pooling and unpooling operations.
  
  Args:
      in_channels (int): Size of each input sample.
      hidden_channels (int): Size of each hidden sample.
      out_channels (int): Size of each output sample.
      depth (int): The depth of the U-Net architecture.
      pool_ratios (float or [float], optional): Graph pooling ratio for each
          depth. (default: :obj:`0.5`)
      sum_res (bool, optional): If set to :obj:`False`, will use
          concatenation for integration of skip connections instead
          summation. (default: :obj:`True`)
      act (torch.nn.functional, optional): The nonlinearity to use.
          (default: :obj:`torch.nn.functional.relu`)
class_docstr_tok: ["The","Graph","U-Net","model","from","the","`","``","Graph","U-Nets","''","<","https",":","\/\/arxiv.org\/abs\/1905.05178",">","`","_","paper","which","implements","a","U-Net","like","architecture","with","graph","pooling","and","unpooling","operations",".","Args",":","in_channels","(","int",")",":","Size","of","each","input","sample",".","hidden_channels","(","int",")",":","Size","of","each","hidden","sample",".","out_channels","(","int",")",":","Size","of","each","output","sample",".","depth","(","int",")",":","The","depth","of","the","U-Net","architecture",".","pool_ratios","(","float","or","[","float","]",",","optional",")",":","Graph","pooling","ratio","for","each","depth",".","(","default",":",":","obj",":","`","0.5","`",")","sum_res","(","bool",",","optional",")",":","If","set","to",":","obj",":","`","False","`",",","will","use","concatenation","for","integration","of","skip","connections","instead","summation",".","(","default",":",":","obj",":","`","True","`",")","act","(","torch.nn.functional",",","optional",")",":","The","nonlinearity","to","use",".","(","default",":",":","obj",":","`","torch.nn.functional.relu","`",")"]
code: def __repr__(self) -> str:
      return (
          f"{self.__class__.__name__}({self.in_channels}, "
          f"{self.hidden_channels}, {self.out_channels}, "
          f"depth={self.depth}, pool_ratios={self.pool_ratios})"
      )
  
file_path: pytorch_geometric/torch_geometric/nn/models/graph_unet.py
imports: from typing import Callable, List, Union
  import torch
  from torch import Tensor
  from torch_geometric.nn import GCNConv, TopKPooling
  from torch_geometric.nn.resolver import activation_resolver
  from torch_geometric.typing import OptTensor, PairTensor
  from torch_geometric.utils import add_self_loops, remove_self_loops, to_torch_csr_tensor
  from torch_geometric.utils.repeat import repeat
label_desc: Use image node_id 5 for calling the GraphUNet obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str, self, str, self, str, self, str