class_docstr: Description
  -----------
  The unpooling module for graph
  
  Parameters
  ----------
  hidden_dim : int
      The number of channels of node features.
class_docstr_tok: ["Description","--","--","--","--","--","-","The","unpooling","module","for","graph","Parameters","--","--","--","--","--","hidden_dim",":","int","The","number","of","channels","of","node","features","."]
docstr: Description
  -----------
  Perform graph unpooling
  
  Parameters
  ----------
  graph : dgl.DGLGraph
      The input graph
  feat : torch.Tensor
      The input node feature
  select_idx : torch.Tensor
      The index in fine graph of node from
      coarse graph, this is obtained from
      previous graph pooling layers.
docstr_tok: ["Description","--","--","--","--","--","-","Perform","graph","unpooling","Parameters","--","--","--","--","--","graph",":","dgl.DGLGraph","The","input","graph","feat",":","torch.Tensor","The","input","node","feature","select_idx",":","torch.Tensor","The","index","in","fine","graph","of","node","from","coarse","graph",",","this","is","obtained","from","previous","graph","pooling","layers","."]
code: def forward(self, graph: DGLGraph, feat: Tensor, select_idx: Tensor):
      """
      Description
      -----------
      Perform graph unpooling
  
      Parameters
      ----------
      graph : dgl.DGLGraph
          The input graph
      feat : torch.Tensor
          The input node feature
      select_idx : torch.Tensor
          The index in fine graph of node from
          coarse graph, this is obtained from
          previous graph pooling layers.
      """
      fine_feat = torch.zeros(
          (graph.num_nodes(), feat.size(-1)), device=feat.device
      )
      fine_feat[select_idx] = feat
      fine_feat = self.up_sample_gcn(graph, fine_feat)
      return fine_feat
  
imports: from typing import Optional
  import dgl
  import torch
  import torch.nn
  from dgl import DGLGraph
  from dgl.nn import GraphConv
  from torch import Tensor
label_desc: Use image node_id 2 for calling the GraphUnpool obj's underlying member method code with example usage: obj.forward(graph, feat, select_idx) and returns: fine_feat