class_docstr: An abstract class for specifying different padding values.
class_docstr_tok: ["An","abstract","class","for","specifying","different","padding","values","."]
code: def __init__(
      self,
      values: Dict[Any, Union[int, float, Padding]],
      default: Union[int, float] = 0.0,
  ):
      if not isinstance(values, dict):
          raise ValueError(
              f"Expected 'values' to be a dictionary "
              f"(got '{type(values)}'"
          )
  
      self.values = {
          key: UniformPadding(val)
          if isinstance(val, (int, float))
          else val
          for key, val in values.items()
      }
      self.default = UniformPadding(default)
  
      for key, value in self.values.items():
          self.validate_key_value(key, 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 1 to create a new MappingPadding object from inherited base classes: Padding with example: obj = MappingPadding(values, default)