def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any]:
        """
        Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
        """
        if isinstance(data, Mapping):
            return type(data)({k: self._prepare_input(v) for k, v in data.items()})
        elif isinstance(data, (tuple, list)):
            return type(data)(self._prepare_input(v) for v in data)
        elif isinstance(data, torch.Tensor):
            kwargs = {"device": self.args.device}
            if self.is_deepspeed_enabled and (torch.is_floating_point(data) or torch.is_complex(data)):
                # NLP models inputs are int/uint and those get adjusted to the right dtype of the
                # embedding. Other models such as wav2vec2's inputs are already float and thus
                # may need special handling to match the dtypes of the model
                kwargs.update({"dtype": self.accelerator.state.deepspeed_plugin.hf_ds_config.dtype()})
            return data.to(**kwargs)
        return data

        Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
        
dpkd/transformers/src/transformers/trainer.py