class_docstr: ZeRO DDP.
  Warning: Nested GeminiDDP is not supported now.
  It is designed to be used with ChunkManager and GeminiManager.
  For more details, see the API reference of ``ChunkManager`` and ``GeminiManager``.
  
  Args:
      module (torch.nn.Module): Module to apply ZeRO-DP.
      gemini_manager (GeminiManager): Manages the chunk manager and heterogeneous memory space.
          For more details, see the API reference of ``GeminiManager``.
      pin_memory (bool): Chunks on CPU Memory use pin-memory.
      force_outputs_fp32 (bool): If set to True, outputs will be fp32. Otherwise, outputs will be fp16.
          Defaults to False.
      strict_ddp_mode (bool): If set to True, there is no tensor sharding, each tensor is replicated.
          Defaults to False. Users can set it to True, when they clearly know that they only need DDP.
      scatter_after_inference (bool): If set to True, the model will be scattered after inference. This will save memory but slow down the consecutive inference.
      mixed_precision (torch.dtype): If set to torch.float16, the model will be trained in fp16. Otherwise, the model will be trained in bf16. Defaults to torch.float16.
docstr: Saves module state to `destination` dictionary, containing a state
  of the module, but not its descendants. This is called on every
  submodule in :meth:`~torch.nn.Module.state_dict`.
  
  In rare cases, subclasses can achieve class-specific behavior by
  overriding this method with custom logic.
  
  Args:
      destination (dict): a dict where state will be stored
      prefix (str): the prefix for parameters and buffers used in this
          module
docstr_tok: ["Saves","module","state","to","`","destination","`","dictionary",",","containing","a","state","of","the","module",",","but","not","its","descendants",".","This","is","called","on","every","submodule","in",":","meth",":","`","~torch.nn.Module.state_dict","`",".","In","rare","cases",",","subclasses","can","achieve","class-specific","behavior","by","overriding","this","method","with","custom","logic",".","Args",":","destination","(","dict",")",":","a","dict","where","state","will","be","stored","prefix","(","str",")",":","the","prefix","for","parameters","and","buffers","used","in","this","module"]
code: def _save_to_state_dict(
      self, destination, prefix, keep_vars, only_rank_0=True
  ):
      r"""Saves module state to `destination` dictionary, containing a state
      of the module, but not its descendants. This is called on every
      submodule in :meth:`~torch.nn.Module.state_dict`.
  
      In rare cases, subclasses can achieve class-specific behavior by
      overriding this method with custom logic.
  
      Args:
          destination (dict): a dict where state will be stored
          prefix (str): the prefix for parameters and buffers used in this
              module
      """
      assert (
          keep_vars is False
      ), "`state_dict` with parameter, `keep_vars=True`, is not supported now."
  
      # get copies of fp32 parameters in CPU
      # as memory of fp16_params may be reused by grad, it's not reliable, we should use fp32_params and convert to fp16
      params = (
          self.fp32_params
          if self.reuse_fp16_chunk
          else self.fp16_params
      )
      param_to_save_data = self._get_param_to_save_data(
          params, only_rank_0
      )
      # get the mapping between copies and fp16 parameters
      p_mapping = dict()
      if self.reuse_fp16_chunk:
          for p, fp32_p in zip(self.fp16_params, self.fp32_params):
              name = self.param2name[p]
              assert (
                  fp32_p in param_to_save_data
              ), "Parameter '{}' is neglected in the chunk list".format(
                  name
              )
              record_parameter = param_to_save_data[fp32_p]
              p_mapping[p] = record_parameter
      else:
          p_mapping = param_to_save_data
      for name, param in self.name2param.items():
          if param is not None:
              if is_ddp_ignored(param):
                  # deal with ddp ignored parameters
                  destination[prefix + name] = (
                      param if keep_vars else param.detach()
                  )
              else:
                  destination[prefix + name] = p_mapping[param]
      del p_mapping
      del param_to_save_data
  
      # save all buffers
      for name, buf in self.named_buffers():
          if (
              buf is not None
              and name not in self._non_persistent_buffers_set
          ):
              destination[prefix + name] = (
                  buf if keep_vars else buf.detach()
              )
      # save extra states
      extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
      if (
          getattr(
              self.__class__,
              "get_extra_state",
              torch.nn.Module.get_extra_state,
          )
          is not torch.nn.Module.get_extra_state
      ):
          destination[extra_state_key] = self.get_extra_state()
  
imports: import itertools
  from collections import OrderedDict
  from contextlib import nullcontext
  from functools import partial
  from typing import Dict, Iterable, Iterator, List, Optional, Set, Tuple, Union
  import torch
  import torch.distributed
  import torch.nn
  from torch.distributed import ProcessGroup
  from torch.distributed.distributed_c10d import _get_default_group
  from colossalai.checkpoint_io.utils import StateDictSharder, gather_distributed_param
  from colossalai.interface import ModelWrapper
  from colossalai.lazy import LazyTensor
  from colossalai.logging import get_dist_logger
  from colossalai.tensor.colo_parameter import ColoParameter
  from colossalai.tensor.d_tensor import distribute_tensor, distribute_tensor_with_customization, get_device_mesh, get_global_shape, get_sharding_spec, init_as_dtensor, init_tensor_as_customization_distributed, is_customized_distributed_tensor, is_distributed_tensor
  from colossalai.tensor.param_op_hook import ColoParamOpHookManager
  from colossalai.utils import _cast_float, free_storage, get_current_device, is_ddp_ignored
  from .chunk import Chunk, ChunkManager, TensorState, init_chunk_manager
  from .gemini_hook import GeminiZeROHook
  from .gemini_mgr import GeminiManager
  from .memory_tracer import MemStats, OrderedParamGenerator
  from .utils import get_temp_total_chunk_on_cuda
label_desc: Use image node_id 23 for calling the GeminiDDP obj's underlying member method code with example usage: obj._save_to_state_dict(destination, prefix, keep_vars, only_rank_0) without return types