code: def load_states_into_optimizer(
      optimizer: Optimizer,
      state_dict: dict,
      id_map: dict,
      strict: bool = False,
  ):
      r"""Copies states from `state_dict` into an Optimizer object.
  
      Args:
          optimizer(Optimizer): An initialized Optimizer object to be loaded
          state_dict(dict): A mapping from tensor index (an integer)
              to its states to be loaded (a mapping from state name to a tensor).
          id_map(dict): A mapping from tensor index (an integer)
              to its corresponding parameter (a tensor) whose states will be updated.
          strict(bool, optional): If set to True, only load the parameters with its id in id_map. Defaults to False.
      """
  
      # Ensure that the keys of state_dict are integers.
      state_dict = {int(k): v for k, v in state_dict.items()}
  
      def cast(param, value, key=None):
          r"""Make a deep copy of value, casting all tensors to device of param."""
          if isinstance(value, torch.Tensor):
              # Floating-point types are a bit special here. They are the only ones
              # that are assumed to always match the type of params.
              # Make sure state['step'] is not casted https://github.com/pytorch/pytorch/issues/74424
              if key != "step":
                  if param.is_floating_point():
                      value = value.to(param.dtype)
                  value = value.to(param.device)
              return value
          elif isinstance(value, dict):
              return {
                  k: cast(param, v, key=k) for k, v in value.items()
              }
          elif isinstance(value, container_abcs.Iterable):
              return type(value)(cast(param, v) for v in value)
          else:
              return value
  
      # Copy state assigned to params (and cast tensors to appropriate types).
      # State that is not assigned to params is copied as is (needed for
      # backward compatibility).
      new_states = defaultdict(dict)
      for k, v in state_dict.items():
          if k in id_map:
              param = id_map[k]
              new_states[param] = cast(param, v)
          elif not strict:
              new_states[k] = v
  
      optimizer.state.update(new_states)
  
imports: import os
  import re
  from collections import abc
  from collections import defaultdict
  from itertools import chain
  from pathlib import Path
  from typing import Iterator, List, Mapping, Optional, OrderedDict, Tuple
  import torch
  import torch.nn
  from packaging.version import Version
  from torch.optim import Optimizer
  from torch.utils._pytree import tree_map
  from colossalai.tensor.d_tensor import is_customized_distributed_tensor, is_distributed_tensor, to_global, to_global_for_customized_distributed_tensor
label_desc: Use image node_id 21 for calling a global function with example usage: load_states_into_optimizer(optimizer, state_dict, id_map, strict) and returns: value, unknown, type, value