code: def _check_output_types(
      output: Tuple[torch.Tensor, torch.Tensor]
  ) -> None:
      y_pred, y = output
      if y_pred.dtype not in (
          torch.float16,
          torch.float32,
          torch.float64,
      ):
          raise TypeError(
              f"Input y_pred dtype should be float 16, 32 or 64, but given {y_pred.dtype}"
          )
  
      if y.dtype not in (torch.float16, torch.float32, torch.float64):
          raise TypeError(
              f"Input y dtype should be float 16, 32 or 64, but given {y.dtype}"
          )
  
file_path: ignite/ignite/contrib/metrics/regression/_base.py
imports: from abc import abstractmethod
  from typing import Tuple
  import torch
  from ignite.metrics import Metric
  from ignite.metrics.metric import reinit__is_reduced
label_desc: Use image node_id 2 for calling a global function with example usage: _check_output_types(output) without return types