class_docstr: Padding dependent on attribute names.
  
  Args:
      values (dict): The mapping from attribute names to padding values.
      default (int or float, optional): The padding value to use for
          attribute names not specified in :obj:`values`.
          (default: :obj:`0.0`)
class_docstr_tok: ["Padding","dependent","on","attribute","names",".","Args",":","values","(","dict",")",":","The","mapping","from","attribute","names","to","padding","values",".","default","(","int","or","float",",","optional",")",":","The","padding","value","to","use","for","attribute","names","not","specified","in",":","obj",":","`","values","`",".","(","default",":",":","obj",":","`","0.0","`",")"]
code: def get_value(
      self,
      store_type: Optional[Union[NodeType, EdgeType]] = None,
      attr_name: Optional[str] = None,
  ) -> Union[int, float]:
      padding = self.values.get(attr_name, self.default)
      return padding.get_value()
  
imports: from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.data.storage import EdgeStorage, NodeStorage
  from torch_geometric.transforms import BaseTransform
  from torch_geometric.typing import EdgeType, NodeType
label_desc: Use image node_id 2 for calling the AttrNamePadding obj's underlying member method code with example usage: obj.get_value(store_type, attr_name) and returns: padding