class_docstr: Converts a sparse adjacency matrix to a dense adjacency matrix with
  shape :obj:`[num_nodes, num_nodes, *]` (functional name: :obj:`to_dense`).
  
  Args:
      num_nodes (int, optional): The number of nodes. If set to :obj:`None`,
          the number of nodes will get automatically inferred.
          (default: :obj:`None`)
class_docstr_tok: ["Converts","a","sparse","adjacency","matrix","to","a","dense","adjacency","matrix","with","shape",":","obj",":","`","[","num_nodes",",","num_nodes",",","*","]","`","(","functional","name",":",":","obj",":","`","to_dense","`",")",".","Args",":","num_nodes","(","int",",","optional",")",":","The","number","of","nodes",".","If","set","to",":","obj",":","`","None","`",",","the","number","of","nodes","will","get","automatically","inferred",".","(","default",":",":","obj",":","`","None","`",")"]
code: def __repr__(self) -> str:
      if self.num_nodes is None:
          return f"{self.__class__.__name__}()"
      return f"{self.__class__.__name__}(num_nodes={self.num_nodes})"
  
file_path: pytorch_geometric/torch_geometric/transforms/to_dense.py
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch_geometric.data import Data
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 3 for calling the ToDense obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str