class_docstr: Mapping class and a torch.nn.Module that registeres its values
  with `self.register_buffer`. Can be indexed like a regular Python
  dictionary, but torch.Tensors it contains are properly registered, and will be visible
  by all Module methods. Supports only `torch.Tensor` as value and str as key.
class_docstr_tok: ["Mapping","class","and","a","torch.nn.Module","that","registeres","its","values","with","`","self.register_buffer","`",".","Can","be","indexed","like","a","regular","Python","dictionary",",","but","torch.Tensors","it","contains","are","properly","registered",",","and","will","be","visible","by","all","Module","methods",".","Supports","only","`","torch.Tensor","`","as","value","and","str","as","key","."]
code: def __getitem__(self, key: str) -> torch.Tensor:
      return getattr(self, key)
  
imports: import logging
  import warnings
  from collections.abc import Mapping
  from dataclasses import dataclass, field
  from distutils.version import LooseVersion
  from typing import Any, Callable, ClassVar, Dict, Iterator, List, Optional, Tuple, Type
  import torch
  from omegaconf import DictConfig
  from pytorch3d.implicitron.tools.config import Configurable, registry, ReplaceableBase, run_auto_creation
  from pytorch3d.structures.volumes import VolumeLocator
  from .utils import interpolate_line, interpolate_plane, interpolate_volume
label_desc: Use image node_id 4 for calling the _RegistratedBufferDict obj's underlying member method code with example usage: obj.__getitem__(key) and returns: getattr