code: def _extract_features(self, inputs: torch.Tensor) -> torch.Tensor:
      inputs = inputs.detach()
  
      if inputs.device != torch.device(self._device):
          inputs = inputs.to(self._device)
  
      with torch.no_grad():
          outputs = self._feature_extractor(inputs).to(
              self._device, dtype=torch.float64
          )
      self._check_feature_shapes(outputs)
  
      return outputs
  
file_path: ignite/ignite/metrics/gan/utils.py
imports: from typing import Callable, Optional, Union
  import torch
  from packaging.version import Version
  from ignite.metrics.metric import Metric
label_desc: Use image node_id 3 for calling the _BaseInceptionMetric obj's underlying member method code with example usage: obj._extract_features(inputs) and returns: outputs