code: def load_state_dict(state_dict):
      # Check if state_dict containes the same number of loss_scalers as current setup
      if len(state_dict) != len(_amp_state.loss_scalers):
          print(
              "Warning: state_dict contains {} entries, while {} loss_scalers are used".format(
                  len(state_dict), len(_amp_state.loss_scalers)
              )
          )
  
      state_dict = state_dict.copy()
  
      nb_loss_scalers = len(_amp_state.loss_scalers)
      unexpected_keys = []
      # Initialize idx outside, since unexpected_keys will increase it if enumerate is used
      idx = 0
      for key in state_dict:
          if "loss_scaler" not in key:
              unexpected_keys.append(key)
          else:
              if idx > (nb_loss_scalers - 1):
                  print(
                      "Skipping loss_scaler[{}], since num_losses was set to {}".format(
                          idx, nb_loss_scalers
                      )
                  )
                  break
              _amp_state.loss_scalers[idx]._loss_scale = state_dict[
                  key
              ]["loss_scale"]
              _amp_state.loss_scalers[idx]._unskipped = state_dict[key][
                  "unskipped"
              ]
              idx += 1
  
      if len(unexpected_keys) > 0:
          raise RuntimeError(
              "Error(s) in loading state_dict. Unexpected key(s) in state_dict: {}. ".format(
                  ", ".join('"{}"'.format(k) for k in unexpected_keys)
              )
          )
  
imports: from collections import OrderedDict
  import torch
  from ._initialize import _initialize
  from ._amp_state import _amp_state, warn_or_err, maybe_print
label_desc: Use image node_id 3 for calling a global function with example usage: load_state_dict(state_dict) without return types