    def load_state_dict(self, state_dict, optimizer_overrides=None):
        """Load an optimizer state dict.

        In general we should prefer the configuration of the existing optimizer
        instance (e.g., learning rate) over that found in the state_dict. This
        allows us to resume training from a checkpoint using a new set of
        optimizer args.
        """
        if "loss_scale" in state_dict and self.scaler is not None:
            self.scaler.loss_scale = state_dict["loss_scale"]
        self.fp32_optimizer.load_state_dict(state_dict, optimizer_overrides)
Load an optimizer state dict.

In general we should prefer the configuration of the existing optimizer
instance (e.g., learning rate) over that found in the state_dict. This
allows us to resume training from a checkpoint using a new set of
optimizer args.
fairseq/optim/fp16_optimizer.py