code: def _assert_half_support(
      metric_module: Metric,
      metric_functional: Callable,
      preds: TEXT_METRIC_INPUT,
      targets: TEXT_METRIC_INPUT,
      device: str = "cpu",
      **kwargs_update: Any,
  ):
      """Test if an metric can be used with half precision tensors.
  
      Args:
          metric_module: the metric module to test
          metric_functional: the metric functional to test
          preds: torch tensor with predictions
          targets: torch tensor with targets
          device: determine device, either "cpu" or "cuda"
          kwargs_update: Additional keyword arguments that will be passed with preds and
                  targets when running update on the metric.
  
      """
      y_hat = preds[0]
      y = targets[0]
      kwargs_update = {
          k: (v[0].half() if v.is_floating_point() else v[0]).to(device)
          if isinstance(v, Tensor)
          else v
          for k, v in kwargs_update.items()
      }
      metric_module = metric_module.to(device)
      _assert_tensor(metric_module(y_hat, y, **kwargs_update))
      _assert_tensor(metric_functional(y_hat, y, **kwargs_update))
  
imports: import pickle
  import sys
  from functools import partial, wraps
  from typing import Any, Callable, Dict, Optional, Sequence, Union
  import numpy
  import pytest
  import torch
  from torch import Tensor
  from torchmetrics import Metric
  from unittests import NUM_PROCESSES
  from unittests.helpers.testers import MetricTester, _assert_allclose, _assert_requires_grad, _assert_tensor
label_desc: Use image node_id 4 for calling a global function with example usage: _assert_half_support(metric_module, metric_functional, preds, targets, device) without return types