class_docstr: A parameter that is initialized from a list of parameters and can be
  turned into a list of views as needed.
class_docstr_tok: ["A","parameter","that","is","initialized","from","a","list","of","parameters","and","can","be","turned","into","a","list","of","views","as","needed","."]
docstr: Return tuple of (names, shapes, numels) metadata for this flat parameter.
docstr_tok: ["Return","tuple","of","(","names",",","shapes",",","numels",")","metadata","for","this","flat","parameter","."]
code: def metadata(self) -> Tuple[List[str], List[torch.Size], List[int]]:
      """Return tuple of (names, shapes, numels) metadata for this flat parameter."""
      names = [
          ".".join([m, n]) if m else n
          for (m, _, n) in self._param_infos
      ]
      return names, self._param_shapes, self._param_numels
  
imports: from contextlib import contextmanager
  from itertools import chain
  import typing
  from typing import TYPE_CHECKING, Any, Dict, Generator, Iterator, List, Mapping, NamedTuple, Optional, Sequence, Set, Tuple, Union, cast
  import torch
  from torch import Tensor
  import torch.nn
  from fairscale.internal.state_dict import replace_by_prefix_
label_desc: Use image node_id 4 for calling the FlatParameter obj's underlying member method code with example usage: obj.metadata() and returns: names,  self,  self