docstr: Loads the optimizer state.
  
  Args:
      state_dict (dict): optimizer state. Should be an object returned
          from a call to :meth:`state_dict`.
code: def load_state_dict(self, state_dict):
      r"""Loads the optimizer state.
  
      Args:
          state_dict (dict): optimizer state. Should be an object returned
              from a call to :meth:`state_dict`.
      """
      # deepcopy, to be consistent with module API
      state_dict = deepcopy(state_dict)
      # Validate the state_dict
      groups = self.param_groups
      saved_groups = state_dict["param_groups"]
  
      if len(groups) != len(saved_groups):
          raise ValueError(
              "loaded state dict has a different number of "
              "parameter groups"
          )
      param_lens = (len(g["params"]) for g in groups)
      saved_lens = (len(g["params"]) for g in saved_groups)
      if any(
          p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)
      ):
          raise ValueError(
              "loaded state dict contains a parameter group "
              "that doesn't match the size of optimizer's group"
          )
  
      # Update the state
      id_map = {
          old_id: p
          for old_id, p in zip(
              chain.from_iterable(g["params"] for g in saved_groups),
              chain.from_iterable(g["params"] for g in groups),
          )
      }
  
      def cast(param, value):
          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.
              if (
                  param.is_floating_point()
                  and value.dtype != torch.uint8
              ):
                  value = value.to(param.dtype)
              return value
          elif isinstance(value, dict):
              for k, v in value.items():
                  if k in self.non_castable_tensor_keys:
                      value[k] = v.to(param.device)
                  else:
                      value[k] = cast(param, v)
  
              return value
          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).
      state = defaultdict(dict)
      for k, v in state_dict["state"].items():
          if k in id_map:
              param = id_map[k]
              state[param] = cast(param, v)
          else:
              state[k] = v
  
      # Update parameter groups, setting their 'params' value
      def update_group(group, new_group):
          new_group["params"] = group["params"]
          return new_group
  
      param_groups = [
          update_group(g, ng) for g, ng in zip(groups, saved_groups)
      ]
      self.__setstate__({"state": state, "param_groups": param_groups})
  
imports: from collections import abc
  from collections import defaultdict
  from copy import deepcopy
  from itertools import chain
  import torch
  import bitsandbytes.functional
label_desc: Use image node_id 4 for calling the Optimizer8bit obj's underlying member method code with example usage: obj.load_state_dict(state_dict) and returns: new_group, value, value, type, value